16_Reinforcement_Learning.ipynb

{
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   "source": [
    "# TensorFlow Tutorial #16\n",
    "# Reinforcement Learning (Q-Learning)\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Introduction\n",
    "\n",
    "This tutorial is about so-called Reinforcement Learning in which an agent is learning how to navigate some environment, in this case Atari games from the 1970-80's. The agent does not know anything about the game and must learn how to play it from trial and error. The only information that is available to the agent is the screen output of the game, and whether the previous action resulted in a reward or penalty.\n",
    "\n",
    "This is a very difficult problem in Machine Learning / Artificial Intelligence, because the agent must both learn to distinguish features in the game-images, and then connect the occurence of certain features in the game-images with its own actions and a reward or penalty that may be deferred many steps into the future.\n",
    "\n",
    "This problem was first solved by the researchers from Google DeepMind. This tutorial is based on the main ideas from their early research papers (especially [this](https://arxiv.org/abs/1312.5602) and [this](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)), although we make several changes because the original DeepMind algorithm was awkward and over-complicated in some ways. But it turns out that you still need several tricks in order to stabilize the training of the agent, so the implementation in this tutorial is unfortunately also somewhat complicated.\n",
    "\n",
    "The basic idea is to have the agent estimate so-called Q-values whenever it sees an image from the game-environment. The Q-values tell the agent which action is most likely to lead to the highest cumulative reward in the future. The problem is then reduced to finding these Q-values and storing them for later retrieval using a function approximator.\n",
    "\n",
    "This builds on some of the previous tutorials. You should be familiar with TensorFlow and Convolutional Neural Networks from Tutorial #01 and #02. It will also be helpful if you are familiar with one of the builder APIs in Tutorials #03 or #03-B."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Problem\n",
    "\n",
    "This tutorial uses the Atari game Breakout, where the player or agent is supposed to hit a ball with a paddle, thus avoiding death while scoring points when the ball smashes pieces of a wall.\n",
    "\n",
    "When a human learns to play a game like this, the first thing to figure out is what part of the game environment you are controlling - in this case the paddle at the bottom. If you move right on the joystick then the paddle moves right and vice versa. The next thing is to figure out what the goal of the game is - in this case to smash as many bricks in the wall as possible so as to maximize the score. Finally you need to learn what to avoid - in this case you must avoid dying by letting the ball pass beside the paddle.\n",
    "\n",
    "Below are shown 3 images from the game that demonstrate what we need our agent to learn. In the image to the left, the ball is going downwards and the agent must learn to move the paddle so as to hit the ball and avoid death. The image in the middle shows the paddle hitting the ball, which eventually leads to the image on the right where the ball smashes some bricks and scores points. The ball then continues downwards and the process repeats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Illustration of the problem](images/16_problem.png)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem is that there are 10 states between the ball going downwards and the paddle hitting the ball, and there are an additional 18 states before the reward is obtained when the ball hits the wall and smashes some bricks. How can we teach an agent to connect these three situations and generalize to similar situations? The answer is to use so-called Reinforcement Learning with a Neural Network, as shown in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q-Learning"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "One of the simplest ways of doing Reinforcement Learning is called Q-learning. Here we want to estimate so-called Q-values which are also called action-values, because they map a state of the game-environment to a numerical value for each possible action that the agent may take. The Q-values indicate which action is expected to result in the highest future reward, thus telling the agent which action to take.\n",
    "\n",
    "Unfortunately we do not know what the Q-values are supposed to be, so we have to estimate them somehow. The Q-values are all initialized to zero and then updated repeatedly as new information is collected from the agent playing the game. When the agent scores a point then the Q-value must be updated with the new information.\n",
    "\n",
    "There are different formulas for updating Q-values, but the simplest is to set the new Q-value to the reward that was observed, plus the maximum Q-value for the following state of the game. This gives the total reward that the agent can expect from the current game-state and onwards. Typically we also multiply the max Q-value for the following state by a so-called discount-factor slightly below 1. This causes more distant rewards to contribute less to the Q-value, thus making the agent favour rewards that are closer in time.\n",
    "\n",
    "The formula for updating the Q-value is:\n",
    "\n",
    "    Q-value for state and action = reward + discount * max Q-value for next state\n",
    "\n",
    "In academic papers, this is typically written with mathematical symbols like this:\n",
    "\n",
    "$$\n",
    "    Q(s_{t},a_{t}) \\leftarrow \\underbrace{r_{t}}_{\\rm reward} + \\underbrace{\\gamma}_{\\rm discount} \\cdot \\underbrace{\\max_{a}Q(s_{t+1}, a)}_{\\rm estimate~of~future~rewards}\n",
    "$$\n",
    "\n",
    "Furthermore, when the agent loses a life, then we know that the future reward is zero because the agent is dead, so we set the Q-value for that state to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Example\n",
    "\n",
    "The images below demonstrate how Q-values are updated in a backwards sweep through the game-states that have previously been visited. In this simple example we assume all Q-values have been initialized to zero. The agent gets a reward of 1 point in the right-most image. This reward is then propagated backwards to the previous game-states, so when we see similar game-states in the future, we know that the given actions resulted in that reward.\n",
    "\n",
    "The discounting is an exponentially decreasing function. This example uses a discount-factor of 0.97 so the Q-value for the 3rd image is about $0.885 \\simeq 0.97^4$ because it is 4 states prior to the state that actually received the reward. Similarly for the other states. This example only shows one Q-value per state, but in reality there is one Q-value for each possible action in the state, and the Q-values are updated in a backwards-sweep using the formula above. This is shown in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Q-values Simple Example](images/16_q-values-simple.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detailed Example\n",
    "\n",
    "This is a more detailed example showing the Q-values for two successive states of the game-environment and how to update them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Q-values Detailed Example](images/16_q-values-details.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state $t$ the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state $t$ and $t+1$ because NOOP means \"No Operation\".\n",
    "\n",
    "In state $t+1$ the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state $t+1$ is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n",
    "\n",
    "Now that we know the reward of taking the NOOP action from state $t$ to $t+1$, we can update the Q-value to incorporate this new information. This uses the formula above:\n",
    "\n",
    "$$\n",
    "    Q(state_{t},NOOP) \\leftarrow \\underbrace{r_{t}}_{\\rm reward} + \\underbrace{\\gamma}_{\\rm discount} \\cdot \\underbrace{\\max_{a}Q(state_{t+1}, a)}_{\\rm estimate~of~future~rewards} = 1.0 + 0.97 \\cdot 1.830 \\simeq 2.775\n",
    "$$\n",
    "\n",
    "The new Q-value is 2.775 which is slightly lower than the previous estimate of 2.900. This Neural Network has already been trained for 150 hours so it is quite good at estimating Q-values, but earlier during the training, the estimated Q-values would be more different.\n",
    "\n",
    "The idea is to have the agent play many, many games and repeatedly update the estimates of the Q-values as more information about rewards and penalties becomes available. This will eventually lead to good estimates of the Q-values, provided the training is numerically stable, as discussed further below. By doing this, we create a connection between rewards and prior actions."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motion Trace\n",
    "\n",
    "If we only use a single image from the game-environment then we cannot tell which direction the ball is moving. The typical solution is to use multiple consecutive images to represent the state of the game-environment.\n",
    "\n",
    "This implementation uses another approach by processing the images from the game-environment in a motion-tracer that outputs two images as shown below. The left image is from the game-environment and the right image is the processed image, which shows traces of recent movements in the game-environment. In this case we can see that the ball is going downwards and has bounced off the right wall, and that the paddle has moved from the left to the right side of the screen.\n",
    "\n",
    "Note that the motion-tracer has only been tested for Breakout and partially tested for Space Invaders, so it may not work for games with more complicated graphics such as Doom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Motion Trace](images/16_motion-trace.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Stability\n",
    "\n",
    "We need a function approximator that can take a state of the game-environment as input and produce as output an estimate of the Q-values for that state. We will use a Convolutional Neural Network for this. Although they have achieved great fame in recent years, they are actually a quite old technologies with many problems - one of which is training stability. A significant part of the research for this tutorial was spent on tuning and stabilizing the training of the Neural Network.\n",
    "\n",
    "To understand why training stability is a problem, consider the 3 images below which show the game-environment in 3 consecutive states. At state $t$ the agent is about to score a point, which happens in the following state $t+1$. Assuming all Q-values were zero prior to this, we should now set the Q-value for state $t+1$ to be 1.0 and it should be 0.97 for state $t$ if the discount-value is 0.97, according to the formula above for updating Q-values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Training Stability](images/16_training_stability.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we were to train a Neural Network to estimate the Q-values for the two states $t$ and $t+1$ with Q-values 0.97 and 1.0, respectively, then the Neural Network will most likely be unable to distinguish properly between the images of these two states. As a result the Neural Network will also estimate a Q-value near 1.0 for state $t+2$ because the images are so similar. But this is clearly wrong because the Q-values for state $t+2$ should be zero as we do not know anything about future rewards at this point, and that is what the Q-values are supposed to estimate.\n",
    "\n",
    "If this is continued and the Neural Network is trained after every new game-state is observed, then it will quickly cause the estimated Q-values to explode. This is an artifact of training Neural Networks which must have sufficiently large and diverse training-sets. For this reason we will use a so-called Replay Memory so we can gather a large number of game-states and shuffle them during training of the Neural Network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flowchart\n",
    "\n",
    "This flowchart shows roughly how Reinforcement Learning is implemented in this tutorial.  There are two main loops which are run sequentially until the Neural Network is sufficiently accurate at estimating Q-values.\n",
    "\n",
    "The first loop is for playing the game and recording data. This uses the Neural Network to estimate Q-values from a game-state. It then stores the game-state along with the corresponding Q-values and reward/penalty in the Replay Memory for later use.\n",
    "\n",
    "The other loop is activated when the Replay Memory is sufficiently full. First it makes a full backwards sweep through the Replay Memory to update the Q-values with the new rewards and penalties that have been observed. Then it performs an optimization run so as to train the Neural Network to better estimate these updated Q-values.\n",
    "\n",
    "There are many more details in the implementation, such as decreasing the learning-rate and increasing the fraction of the Replay Memory being used during training, but this flowchart shows the main ideas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Flowchart](images/16_flowchart.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Architecture\n",
    "\n",
    "The Neural Network used in this implementation has 3 convolutional layers, all of which have filter-size 3x3. The layers have 16, 32, and 64 output channels, respectively. The stride is 2 in the first two convolutional layers and 1 in the last layer.\n",
    "\n",
    "Following the 3 convolutional layers there are 4 fully-connected layers each with 1024 units and ReLU-activation. Then there is a single fully-connected layer with linear activation used as the output of the Neural Network.\n",
    "\n",
    "This architecture is different from those typically used in research papers from DeepMind and others. They often have large convolutional filter-sizes of 8x8 and 4x4 with high stride-values. This causes more aggressive down-sampling of the game-state images. They also typically have only a single fully-connected layer with 256 or 512 ReLU units.\n",
    "\n",
    "During the research for this tutorial, it was found that smaller filter-sizes and strides in the convolutional layers, combined with several fully-connected layers having more units, were necessary in order to have sufficiently accurate Q-values. The Neural Network architectures originally used by DeepMind appear to distort the Q-values quite significantly. A reason that their approach still worked, is possibly due to their use of a very large Replay Memory with 1 million states, and that the Neural Network did one mini-batch of training for each step of the game-environment, and some other tricks.\n",
    "\n",
    "The architecture used here is probably excessive but it takes several days of training to test each architecture, so it is left as an exercise for the reader to try and find a smaller Neural Network architecture that still performs well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [documentation](https://github.com/openai/gym) for OpenAI Gym currently suggests that you need to build it in order to install it. But if you just want to install the Atari games, then you only need to install a single pip-package by typing the following commands in a terminal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- conda create --name tf-gym --clone tf\n",
    "- source activate tf-gym\n",
    "- pip install gym[atari]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This assumes you already have an Anaconda environment named `tf` which has TensorFlow installed, it will then be cloned to another environment named `tf-gym` where OpenAI Gym is also installed. This allows you to easily switch between your normal TensorFlow environment and another one which also contains OpenAI Gym."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also have two environments named `tf-gpu` and `tf-gpu-gym` for the GPU versions of TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow 2\n",
    "\n",
    "This tutorial was developed using TensorFlow v.1 back in the year 2016-2017. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\". It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xu2SVpFJjmJr"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import gym\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "# Use TensorFlow v.2 with this old v.1 code.\n",
    "# E.g. placeholder variables and sessions have changed in TF2.\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main source-code for Reinforcement Learning is located in the following module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import reinforcement_learning as rl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was developed using Python 3.6.0 (Anaconda) with package versions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorFlow\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.17.1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OpenAI Gym\n",
    "gym.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Game Environment\n",
    "\n",
    "This is the name of the game-environment that we want to use in OpenAI Gym."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_name = 'Breakout-v0'\n",
    "# env_name = 'SpaceInvaders-v0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the base-directory for the TensorFlow checkpoints as well as various log-files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rl.checkpoint_base_dir = 'checkpoints_tutorial16/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the base-dir has been set, you need to call this function to set all the paths that will be used. This will also create the checkpoint-dir if it does not already exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rl.update_paths(env_name=env_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Pre-Trained Model\n",
    "\n",
    "The original version of this tutorial provided some TensorFlow checkpoints with pre-trained models for download. But due to changes in both TensorFlow and OpenAI Gym, these pre-trained models cannot be loaded anymore so they have been deleted from the web-server. You will therefore have to train your own model further below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Agent\n",
    "\n",
    "The Agent-class implements the main loop for playing the game, recording data and optimizing the Neural Network. We create an object-instance and need to set `training=True` because we want to use the replay-memory to record states and Q-values for plotting further below. We disable logging so this does not corrupt the logs from the actual training that was done previously. We can also set `render=True` but it will have no effect as long as `training==True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1189: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1205: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Flatten instead.\n",
      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1209: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Trying to restore last checkpoint ...\n",
      "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n",
      "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n"
     ]
    }
   ],
   "source": [
    "agent = rl.Agent(env_name=env_name,\n",
    "                 training=True,\n",
    "                 render=True,\n",
    "                 use_logging=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Neural Network is automatically instantiated by the Agent-class. We will create a direct reference for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = agent.model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, the Agent-class also allocates the replay-memory when `training==True`. The replay-memory will require more than 3 GB of RAM, so it should only be allocated when needed. We will need the replay-memory in this Notebook to record the states and Q-values we observe, so they can be plotted further below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "replay_memory = agent.replay_memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "The agent's `run()` function is used to play the game. This uses the Neural Network to estimate Q-values and hence determine the agent's actions. If `training==True` then it will also gather states and Q-values in the replay-memory and train the Neural Network when the replay-memory is sufficiently full. You can set `num_episodes=None` if you want an infinite loop that you would stop manually with `ctrl-c`. In this case we just set `num_episodes=1` because we are not actually interested in training the Neural Network any further, we merely want to collect some states and Q-values in the replay-memory so we can plot them below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2388:1176704\t Epsilon: 0.10\t Reward: 26.0\t Episode Mean: 26.0\n"
     ]
    }
   ],
   "source": [
    "agent.run(num_episodes=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In training-mode, this function will output a line for each episode. The first counter is for the number of episodes that have been processed. The second counter is for the number of states that have been processed. These two counters are stored in the TensorFlow checkpoint along with the weights of the Neural Network, so you can restart the training e.g. if you only have one computer and need to train during the night.\n",
    "\n",
    "Note that the number of episodes is almost 90k. It is impractical to print that many lines in this Notebook, so the training is better done in a terminal window by running the following commands:\n",
    "\n",
    "```\n",
    "source activate tf-gpu-gym  # Activate your Python environment with TF and Gym.\n",
    "python reinforcement_learning.py --env Breakout-v0 --training\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is being logged during training so we can plot the progress afterwards. The reward for each episode and a running mean of the last 30 episodes are logged to file. Basic statistics for the Q-values in the replay-memory are also logged to file before each optimization run.\n",
    "\n",
    "This could be logged using TensorFlow and TensorBoard, but they were designed for logging variables of the TensorFlow graph and data that flows through the graph. In this case the data we want logged does not reside in the graph, so it becomes a bit awkward to use TensorFlow to log this data.\n",
    "\n",
    "We have therefore implemented a few small classes that can write and read these logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_q_values = rl.LogQValues()\n",
    "log_reward = rl.LogReward()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now read the logs from file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "log_q_values.read()\n",
    "log_reward.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Progress: Reward\n",
    "\n",
    "This plot shows the reward for each episode during training, as well as the running mean of the last 30 episodes. Note how the reward varies greatly from one episode to the next, so it is difficult to say from this plot alone whether the agent is really improving during the training, although the running mean does appear to trend upwards slightly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log_reward.count_states, log_reward.episode, label='Episode Reward')\n",
    "plt.plot(log_reward.count_states, log_reward.mean, label='Mean of 30 episodes')\n",
    "plt.xlabel('State-Count for Game Environment')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Progress: Q-Values\n",
    "\n",
    "The following plot shows the mean Q-values from the replay-memory prior to each run of the optimizer for the Neural Network. Note how the mean Q-values increase rapidly in the beginning and then they increase fairly steadily for 40 million states, after which they still trend upwards but somewhat more irregularly.\n",
    "\n",
    "The fast improvement in the beginning is probably due to (1) the use of a smaller replay-memory early in training so the Neural Network is optimized more often and the new information is used faster, (2) the backwards-sweeping of the replay-memory so the rewards are used to update the Q-values for many of the states, instead of just updating the Q-values for a single state, and (3) the replay-memory is balanced so at least half of each mini-batch contains states whose Q-values have high estimation-errors for the Neural Network.\n",
    "\n",
    "The [original paper from DeepMind](https://arxiv.org/abs/1312.5602) showed much slower progress in the first phase of training, see Figure 2 in that paper but note that the Q-values are not directly comparable, possibly because they used a higher discount factor of 0.99 while we only used 0.97 here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log_q_values.count_states, log_q_values.mean, label='Q-Value Mean')\n",
    "plt.xlabel('State-Count for Game Environment')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n",
    "\n",
    "When the agent and Neural Network is being trained, the so-called epsilon-probability is typically decreased from 1.0 to 0.1 over a large number of steps, after which the probability is held fixed at 0.1. This means the probability is 0.1 or 10% that the agent will select a random action in each step, otherwise it will select the action that has the highest Q-value. This is known as the epsilon-greedy policy. The choice of 0.1 for the epsilon-probability is a compromise between taking the actions that are already known to be good, versus exploring new actions that might lead to even higher rewards or might lead to death of the agent.\n",
    "\n",
    "During testing it is common to lower the epsilon-probability even further. We have set it to 0.01 as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.epsilon_greedy.epsilon_testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now instruct the agent that it should no longer perform training by setting this boolean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.training = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also reset the previous episode rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.reset_episode_rewards()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can render the game-environment to screen so we can see the agent playing the game, by setting this boolean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.render = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run a single episode by calling the `run()` function again. This should open a new window that shows the game being played by the agent. At the time of this writing, it was not possible to resize this tiny window, and the developers at OpenAI did not seem to care about this feature which should obviously be there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2390:1176749\tQ-min: 1.247\tQ-max: 1.411\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
      "2390:1176802\tQ-min: 1.227\tQ-max: 1.425\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
      "2390:1176845\tQ-min: 0.109\tQ-max: 0.144\tLives: 4\tReward: 2.0\tEpisode Mean: 0.0\n",
      "2390:1176899\tQ-min: 1.184\tQ-max: 1.423\tLives: 4\tReward: 3.0\tEpisode Mean: 0.0\n",
      "2390:1176954\tQ-min: 1.336\tQ-max: 1.472\tLives: 4\tReward: 4.0\tEpisode Mean: 0.0\n",
      "2390:1177004\tQ-min: 1.303\tQ-max: 1.382\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n",
      "2390:1177050\tQ-min: 1.247\tQ-max: 1.539\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n",
      "2390:1177070\tQ-min: 0.140\tQ-max: 0.149\tLives: 3\tReward: 6.0\tEpisode Mean: 0.0\n",
      "2390:1177123\tQ-min: 1.260\tQ-max: 1.348\tLives: 3\tReward: 7.0\tEpisode Mean: 0.0\n",
      "2390:1177171\tQ-min: 1.212\tQ-max: 1.473\tLives: 3\tReward: 8.0\tEpisode Mean: 0.0\n",
      "2390:1177227\tQ-min: 1.333\tQ-max: 1.445\tLives: 3\tReward: 9.0\tEpisode Mean: 0.0\n",
      "2390:1177273\tQ-min: 1.285\tQ-max: 1.542\tLives: 3\tReward: 10.0\tEpisode Mean: 0.0\n",
      "2390:1177304\tQ-min: 1.227\tQ-max: 1.538\tLives: 3\tReward: 11.0\tEpisode Mean: 0.0\n",
      "2390:1177339\tQ-min: 1.256\tQ-max: 1.539\tLives: 3\tReward: 12.0\tEpisode Mean: 0.0\n",
      "2390:1177359\tQ-min: 0.078\tQ-max: 0.126\tLives: 2\tReward: 12.0\tEpisode Mean: 0.0\n",
      "2390:1177417\tQ-min: 1.150\tQ-max: 1.406\tLives: 2\tReward: 13.0\tEpisode Mean: 0.0\n",
      "2390:1177469\tQ-min: 1.298\tQ-max: 1.452\tLives: 2\tReward: 14.0\tEpisode Mean: 0.0\n",
      "2390:1177530\tQ-min: 1.229\tQ-max: 1.372\tLives: 2\tReward: 15.0\tEpisode Mean: 0.0\n",
      "2390:1177571\tQ-min: 0.060\tQ-max: 0.104\tLives: 1\tReward: 15.0\tEpisode Mean: 0.0\n",
      "2390:1177617\tQ-min: 1.266\tQ-max: 1.462\tLives: 1\tReward: 16.0\tEpisode Mean: 0.0\n",
      "2390:1177668\tQ-min: 1.182\tQ-max: 1.566\tLives: 1\tReward: 20.0\tEpisode Mean: 0.0\n",
      "2390:1177727\tQ-min: 1.250\tQ-max: 1.491\tLives: 1\tReward: 21.0\tEpisode Mean: 0.0\n",
      "2390:1177781\tQ-min: 1.172\tQ-max: 1.604\tLives: 1\tReward: 25.0\tEpisode Mean: 0.0\n",
      "2390:1177796\tQ-min: 0.434\tQ-max: 0.717\tLives: 0\tReward: 25.0\tEpisode Mean: 25.0\n"
     ]
    }
   ],
   "source": [
    "agent.run(num_episodes=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Reward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The game-play is slightly random, both with regard to selecting actions using the epsilon-greedy policy, but also because the OpenAI Gym environment will repeat any action between 2-4 times, with the number chosen at random. So the reward of one episode is not an accurate estimate of the reward that can be expected in general from this agent.\n",
    "\n",
    "We need to run 30 or even 50 episodes to get a more accurate estimate of the reward that can be expected.\n",
    "\n",
    "We will first reset the previous episode rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.reset_episode_rewards()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We disable the screen-rendering so the game-environment runs much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.render = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run 30 episodes. This records the rewards for each episode. It might have been a good idea to disable the output so it does not print all these lines - you can do this as an exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2392:1177839\tQ-min: 1.184\tQ-max: 1.365\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
      "2392:1177890\tQ-min: 1.239\tQ-max: 1.387\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
      "2392:1177953\tQ-min: 1.205\tQ-max: 1.420\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n",
      "2392:1177999\tQ-min: 1.243\tQ-max: 1.541\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n",
      "2392:1178032\tQ-min: 1.236\tQ-max: 1.516\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n",
      "2392:1178055\tQ-min: 0.050\tQ-max: 0.106\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n",
      "2392:1178106\tQ-min: 1.229\tQ-max: 1.348\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n",
      "2392:1178147\tQ-min: 0.103\tQ-max: 0.128\tLives: 3\tReward: 6.0\tEpisode Mean: 0.0\n",
      "2392:1178205\tQ-min: 1.239\tQ-max: 1.342\tLives: 3\tReward: 7.0\tEpisode Mean: 0.0\n",
      "2392:1178269\tQ-min: 1.254\tQ-max: 1.491\tLives: 3\tReward: 8.0\tEpisode Mean: 0.0\n",
      "2392:1178308\tQ-min: 0.082\tQ-max: 0.123\tLives: 2\tReward: 8.0\tEpisode Mean: 0.0\n",
      "2392:1178355\tQ-min: 1.247\tQ-max: 1.398\tLives: 2\tReward: 9.0\tEpisode Mean: 0.0\n",
      "2392:1178382\tQ-min: 0.131\tQ-max: 0.157\tLives: 1\tReward: 9.0\tEpisode Mean: 0.0\n",
      "2392:1178441\tQ-min: 1.198\tQ-max: 1.503\tLives: 1\tReward: 10.0\tEpisode Mean: 0.0\n",
      "2392:1178506\tQ-min: 1.218\tQ-max: 1.342\tLives: 1\tReward: 11.0\tEpisode Mean: 0.0\n",
      "2392:1178573\tQ-min: 1.211\tQ-max: 1.554\tLives: 1\tReward: 12.0\tEpisode Mean: 0.0\n",
      "2392:1178628\tQ-min: 1.272\tQ-max: 1.546\tLives: 1\tReward: 13.0\tEpisode Mean: 0.0\n",
      "2392:1178650\tQ-min: 0.079\tQ-max: 0.122\tLives: 0\tReward: 13.0\tEpisode Mean: 13.0\n",
      "2393:1178697\tQ-min: 1.203\tQ-max: 1.427\tLives: 5\tReward: 1.0\tEpisode Mean: 13.0\n",
      "2393:1178739\tQ-min: 1.233\tQ-max: 1.548\tLives: 5\tReward: 2.0\tEpisode Mean: 13.0\n",
      "2393:1178793\tQ-min: 1.309\tQ-max: 1.414\tLives: 5\tReward: 3.0\tEpisode Mean: 13.0\n",
      "2393:1178835\tQ-min: 0.102\tQ-max: 0.131\tLives: 4\tReward: 3.0\tEpisode Mean: 13.0\n",
      "2393:1178878\tQ-min: 1.257\tQ-max: 1.521\tLives: 4\tReward: 4.0\tEpisode Mean: 13.0\n",
      "2393:1178921\tQ-min: 1.275\tQ-max: 1.446\tLives: 4\tReward: 5.0\tEpisode Mean: 13.0\n",
      "2393:1178966\tQ-min: 1.297\tQ-max: 1.528\tLives: 4\tReward: 6.0\tEpisode Mean: 13.0\n",
      "2393:1178997\tQ-min: 0.083\tQ-max: 0.126\tLives: 3\tReward: 6.0\tEpisode Mean: 13.0\n",
      "2393:1179043\tQ-min: 1.246\tQ-max: 1.419\tLives: 3\tReward: 7.0\tEpisode Mean: 13.0\n",
      "2393:1179098\tQ-min: 1.231\tQ-max: 1.501\tLives: 3\tReward: 8.0\tEpisode Mean: 13.0\n",
      "2393:1179151\tQ-min: 1.264\tQ-max: 1.522\tLives: 3\tReward: 9.0\tEpisode Mean: 13.0\n",
      "2393:1179183\tQ-min: 0.069\tQ-max: 0.107\tLives: 2\tReward: 9.0\tEpisode Mean: 13.0\n",
      "2393:1179239\tQ-min: 1.253\tQ-max: 1.325\tLives: 2\tReward: 10.0\tEpisode Mean: 13.0\n",
      "2393:1179305\tQ-min: 1.280\tQ-max: 1.464\tLives: 2\tReward: 14.0\tEpisode Mean: 13.0\n",
      "2393:1179350\tQ-min: 0.060\tQ-max: 0.100\tLives: 1\tReward: 14.0\tEpisode Mean: 13.0\n",
      "2393:1179390\tQ-min: 1.216\tQ-max: 1.519\tLives: 1\tReward: 15.0\tEpisode Mean: 13.0\n",
      "2393:1179432\tQ-min: 1.231\tQ-max: 1.558\tLives: 1\tReward: 16.0\tEpisode Mean: 13.0\n",
      "2393:1179478\tQ-min: 1.285\tQ-max: 1.511\tLives: 1\tReward: 17.0\tEpisode Mean: 13.0\n",
      "2393:1179517\tQ-min: 1.237\tQ-max: 1.543\tLives: 1\tReward: 18.0\tEpisode Mean: 13.0\n",
      "2393:1179549\tQ-min: 1.248\tQ-max: 1.507\tLives: 1\tReward: 19.0\tEpisode Mean: 13.0\n",
      "2393:1179584\tQ-min: 1.236\tQ-max: 1.507\tLives: 1\tReward: 20.0\tEpisode Mean: 13.0\n",
      "2393:1179606\tQ-min: 0.049\tQ-max: 0.105\tLives: 0\tReward: 20.0\tEpisode Mean: 16.5\n",
      "2394:1179648\tQ-min: 1.256\tQ-max: 1.476\tLives: 5\tReward: 1.0\tEpisode Mean: 16.5\n",
      "2394:1179700\tQ-min: 1.234\tQ-max: 1.445\tLives: 5\tReward: 2.0\tEpisode Mean: 16.5\n",
      "2394:1179738\tQ-min: 0.107\tQ-max: 0.141\tLives: 4\tReward: 2.0\tEpisode Mean: 16.5\n",
      "2394:1179783\tQ-min: 1.214\tQ-max: 1.541\tLives: 4\tReward: 3.0\tEpisode Mean: 16.5\n",
      "2394:1179836\tQ-min: 1.240\tQ-max: 1.416\tLives: 4\tReward: 4.0\tEpisode Mean: 16.5\n",
      "2394:1179889\tQ-min: 1.260\tQ-max: 1.504\tLives: 4\tReward: 5.0\tEpisode Mean: 16.5\n",
      "2394:1179925\tQ-min: 1.334\tQ-max: 1.603\tLives: 4\tReward: 6.0\tEpisode Mean: 16.5\n",
      "2394:1179947\tQ-min: 0.073\tQ-max: 0.119\tLives: 3\tReward: 6.0\tEpisode Mean: 16.5\n",
      "2394:1179992\tQ-min: 1.246\tQ-max: 1.600\tLives: 3\tReward: 7.0\tEpisode Mean: 16.5\n",
      "2394:1180045\tQ-min: 1.220\tQ-max: 1.485\tLives: 3\tReward: 8.0\tEpisode Mean: 16.5\n",
      "2394:1180108\tQ-min: 1.235\tQ-max: 1.397\tLives: 3\tReward: 9.0\tEpisode Mean: 16.5\n",
      "2394:1180153\tQ-min: 1.245\tQ-max: 1.484\tLives: 3\tReward: 10.0\tEpisode Mean: 16.5\n",
      "2394:1180184\tQ-min: 1.274\tQ-max: 1.610\tLives: 3\tReward: 11.0\tEpisode Mean: 16.5\n",
      "2394:1180216\tQ-min: 1.277\tQ-max: 1.399\tLives: 3\tReward: 12.0\tEpisode Mean: 16.5\n",
      "2394:1180248\tQ-min: 1.279\tQ-max: 1.556\tLives: 3\tReward: 13.0\tEpisode Mean: 16.5\n",
      "2394:1180268\tQ-min: 0.142\tQ-max: 0.154\tLives: 2\tReward: 13.0\tEpisode Mean: 16.5\n",
      "2394:1180301\tQ-min: 0.085\tQ-max: 0.113\tLives: 1\tReward: 13.0\tEpisode Mean: 16.5\n",
      "2394:1180355\tQ-min: 1.252\tQ-max: 1.359\tLives: 1\tReward: 14.0\tEpisode Mean: 16.5\n",
      "2394:1180420\tQ-min: 1.216\tQ-max: 1.448\tLives: 1\tReward: 15.0\tEpisode Mean: 16.5\n",
      "2394:1180464\tQ-min: 0.038\tQ-max: 0.105\tLives: 0\tReward: 15.0\tEpisode Mean: 16.0\n",
      "2395:1180508\tQ-min: 1.243\tQ-max: 1.442\tLives: 5\tReward: 1.0\tEpisode Mean: 16.0\n",
      "2395:1180536\tQ-min: 0.075\tQ-max: 0.113\tLives: 4\tReward: 1.0\tEpisode Mean: 16.0\n",
      "2395:1180594\tQ-min: 1.224\tQ-max: 1.365\tLives: 4\tReward: 2.0\tEpisode Mean: 16.0\n",
      "2395:1180635\tQ-min: 0.088\tQ-max: 0.131\tLives: 3\tReward: 2.0\tEpisode Mean: 16.0\n",
      "2395:1180678\tQ-min: 1.234\tQ-max: 1.464\tLives: 3\tReward: 3.0\tEpisode Mean: 16.0\n",
      "2395:1180730\tQ-min: 1.274\tQ-max: 1.366\tLives: 3\tReward: 4.0\tEpisode Mean: 16.0\n",
      "2395:1180792\tQ-min: 1.223\tQ-max: 1.372\tLives: 3\tReward: 5.0\tEpisode Mean: 16.0\n",
      "2395:1180841\tQ-min: 1.232\tQ-max: 1.580\tLives: 3\tReward: 6.0\tEpisode Mean: 16.0\n",
      "2395:1180876\tQ-min: 1.283\tQ-max: 1.449\tLives: 3\tReward: 7.0\tEpisode Mean: 16.0\n",
      "2395:1180911\tQ-min: 1.224\tQ-max: 1.545\tLives: 3\tReward: 11.0\tEpisode Mean: 16.0\n",
      "2395:1180934\tQ-min: 0.094\tQ-max: 0.122\tLives: 2\tReward: 11.0\tEpisode Mean: 16.0\n",
      "2395:1180979\tQ-min: 1.259\tQ-max: 1.421\tLives: 2\tReward: 12.0\tEpisode Mean: 16.0\n",
      "2395:1181005\tQ-min: 0.070\tQ-max: 0.112\tLives: 1\tReward: 12.0\tEpisode Mean: 16.0\n",
      "2395:1181062\tQ-min: 1.235\tQ-max: 1.389\tLives: 1\tReward: 13.0\tEpisode Mean: 16.0\n",
      "2395:1181114\tQ-min: 1.251\tQ-max: 1.598\tLives: 1\tReward: 14.0\tEpisode Mean: 16.0\n",
      "2395:1181173\tQ-min: 1.195\tQ-max: 1.431\tLives: 1\tReward: 15.0\tEpisode Mean: 16.0\n",
      "2395:1181215\tQ-min: 0.102\tQ-max: 0.136\tLives: 0\tReward: 15.0\tEpisode Mean: 15.8\n",
      "2396:1181268\tQ-min: 1.211\tQ-max: 1.397\tLives: 5\tReward: 1.0\tEpisode Mean: 15.8\n",
      "2396:1181331\tQ-min: 1.216\tQ-max: 1.481\tLives: 5\tReward: 2.0\tEpisode Mean: 15.8\n",
      "2396:1181398\tQ-min: 1.215\tQ-max: 1.386\tLives: 5\tReward: 3.0\tEpisode Mean: 15.8\n",
      "2396:1181446\tQ-min: 1.279\tQ-max: 1.453\tLives: 5\tReward: 4.0\tEpisode Mean: 15.8\n",
      "2396:1181464\tQ-min: 0.236\tQ-max: 0.240\tLives: 4\tReward: 4.0\tEpisode Mean: 15.8\n",
      "2396:1181521\tQ-min: 1.202\tQ-max: 1.430\tLives: 4\tReward: 5.0\tEpisode Mean: 15.8\n",
      "2396:1181570\tQ-min: 1.263\tQ-max: 1.558\tLives: 4\tReward: 6.0\tEpisode Mean: 15.8\n",
      "2396:1181620\tQ-min: 1.257\tQ-max: 1.536\tLives: 4\tReward: 7.0\tEpisode Mean: 15.8\n",
      "2396:1181665\tQ-min: 1.262\tQ-max: 1.546\tLives: 4\tReward: 8.0\tEpisode Mean: 15.8\n",
      "2396:1181697\tQ-min: 1.265\tQ-max: 1.603\tLives: 4\tReward: 9.0\tEpisode Mean: 15.8\n",
      "2396:1181733\tQ-min: 1.242\tQ-max: 1.638\tLives: 4\tReward: 10.0\tEpisode Mean: 15.8\n",
      "2396:1181763\tQ-min: 1.220\tQ-max: 1.614\tLives: 4\tReward: 11.0\tEpisode Mean: 15.8\n",
      "2396:1181811\tQ-min: 1.219\tQ-max: 1.439\tLives: 4\tReward: 12.0\tEpisode Mean: 15.8\n",
      "2396:1181852\tQ-min: 0.090\tQ-max: 0.128\tLives: 3\tReward: 12.0\tEpisode Mean: 15.8\n",
      "2396:1181897\tQ-min: 1.292\tQ-max: 1.475\tLives: 3\tReward: 13.0\tEpisode Mean: 15.8\n",
      "2396:1181948\tQ-min: 1.281\tQ-max: 1.448\tLives: 3\tReward: 14.0\tEpisode Mean: 15.8\n",
      "2396:1181992\tQ-min: 0.110\tQ-max: 0.142\tLives: 2\tReward: 14.0\tEpisode Mean: 15.8\n",
      "2396:1182050\tQ-min: 1.275\tQ-max: 1.405\tLives: 2\tReward: 15.0\tEpisode Mean: 15.8\n",
      "2396:1182093\tQ-min: 0.132\tQ-max: 0.143\tLives: 1\tReward: 15.0\tEpisode Mean: 15.8\n",
      "2396:1182154\tQ-min: 1.196\tQ-max: 1.422\tLives: 1\tReward: 16.0\tEpisode Mean: 15.8\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2397:1182556\tQ-min: 1.242\tQ-max: 1.360\tLives: 3\tReward: 5.0\tEpisode Mean: 16.0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2406:1191546\tQ-min: 1.255\tQ-max: 1.417\tLives: 5\tReward: 1.0\tEpisode Mean: 20.1\n",
      "2406:1191594\tQ-min: 1.221\tQ-max: 1.351\tLives: 5\tReward: 2.0\tEpisode Mean: 20.1\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2410:1196191\tQ-min: 1.271\tQ-max: 1.507\tLives: 1\tReward: 15.0\tEpisode Mean: 20.0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2415:1200760\tQ-min: 1.242\tQ-max: 1.342\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n",
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      "2416:1201771\tQ-min: 0.088\tQ-max: 0.128\tLives: 3\tReward: 4.0\tEpisode Mean: 20.3\n",
      "2416:1201803\tQ-min: 0.119\tQ-max: 0.135\tLives: 2\tReward: 4.0\tEpisode Mean: 20.3\n",
      "2416:1201857\tQ-min: 1.236\tQ-max: 1.334\tLives: 2\tReward: 5.0\tEpisode Mean: 20.3\n",
      "2416:1201899\tQ-min: 0.089\tQ-max: 0.126\tLives: 1\tReward: 5.0\tEpisode Mean: 20.3\n",
      "2416:1201953\tQ-min: 1.228\tQ-max: 1.334\tLives: 1\tReward: 6.0\tEpisode Mean: 20.3\n",
      "2416:1202016\tQ-min: 1.273\tQ-max: 1.509\tLives: 1\tReward: 7.0\tEpisode Mean: 20.3\n",
      "2416:1202069\tQ-min: 1.240\tQ-max: 1.469\tLives: 1\tReward: 8.0\tEpisode Mean: 20.3\n",
      "2416:1202105\tQ-min: 1.296\tQ-max: 1.674\tLives: 1\tReward: 9.0\tEpisode Mean: 20.3\n",
      "2416:1202138\tQ-min: 1.194\tQ-max: 1.506\tLives: 1\tReward: 13.0\tEpisode Mean: 20.3\n",
      "2416:1202161\tQ-min: 0.081\tQ-max: 0.119\tLives: 0\tReward: 13.0\tEpisode Mean: 20.0\n",
      "2417:1202206\tQ-min: 1.214\tQ-max: 1.396\tLives: 5\tReward: 1.0\tEpisode Mean: 20.0\n",
      "2417:1202233\tQ-min: 0.057\tQ-max: 0.112\tLives: 4\tReward: 1.0\tEpisode Mean: 20.0\n",
      "2417:1202277\tQ-min: 1.269\tQ-max: 1.533\tLives: 4\tReward: 2.0\tEpisode Mean: 20.0\n",
      "2417:1202306\tQ-min: 0.072\tQ-max: 0.115\tLives: 3\tReward: 2.0\tEpisode Mean: 20.0\n",
      "2417:1202350\tQ-min: 1.227\tQ-max: 1.585\tLives: 3\tReward: 3.0\tEpisode Mean: 20.0\n",
      "2417:1202406\tQ-min: 1.288\tQ-max: 1.457\tLives: 3\tReward: 4.0\tEpisode Mean: 20.0\n",
      "2417:1202468\tQ-min: 1.253\tQ-max: 1.385\tLives: 3\tReward: 5.0\tEpisode Mean: 20.0\n",
      "2417:1202517\tQ-min: 1.319\tQ-max: 1.524\tLives: 3\tReward: 6.0\tEpisode Mean: 20.0\n",
      "2417:1202550\tQ-min: 1.255\tQ-max: 1.540\tLives: 3\tReward: 7.0\tEpisode Mean: 20.0\n",
      "2417:1202581\tQ-min: 1.207\tQ-max: 1.530\tLives: 3\tReward: 8.0\tEpisode Mean: 20.0\n",
      "2417:1202613\tQ-min: 1.282\tQ-max: 1.589\tLives: 3\tReward: 9.0\tEpisode Mean: 20.0\n",
      "2417:1202660\tQ-min: 1.231\tQ-max: 1.373\tLives: 3\tReward: 10.0\tEpisode Mean: 20.0\n",
      "2417:1202702\tQ-min: 0.106\tQ-max: 0.128\tLives: 2\tReward: 10.0\tEpisode Mean: 20.0\n",
      "2417:1202749\tQ-min: 1.240\tQ-max: 1.517\tLives: 2\tReward: 11.0\tEpisode Mean: 20.0\n",
      "2417:1202806\tQ-min: 1.243\tQ-max: 1.412\tLives: 2\tReward: 12.0\tEpisode Mean: 20.0\n",
      "2417:1202873\tQ-min: 1.192\tQ-max: 1.475\tLives: 2\tReward: 13.0\tEpisode Mean: 20.0\n",
      "2417:1202916\tQ-min: 0.107\tQ-max: 0.135\tLives: 1\tReward: 13.0\tEpisode Mean: 20.0\n",
      "2417:1202973\tQ-min: 1.179\tQ-max: 1.458\tLives: 1\tReward: 14.0\tEpisode Mean: 20.0\n",
      "2417:1203037\tQ-min: 1.230\tQ-max: 1.436\tLives: 1\tReward: 15.0\tEpisode Mean: 20.0\n",
      "2417:1203108\tQ-min: 1.138\tQ-max: 1.512\tLives: 1\tReward: 16.0\tEpisode Mean: 20.0\n",
      "2417:1203151\tQ-min: 0.098\tQ-max: 0.119\tLives: 0\tReward: 16.0\tEpisode Mean: 19.9\n",
      "2418:1203192\tQ-min: 1.241\tQ-max: 1.486\tLives: 5\tReward: 1.0\tEpisode Mean: 19.9\n",
      "2418:1203237\tQ-min: 1.238\tQ-max: 1.490\tLives: 5\tReward: 2.0\tEpisode Mean: 19.9\n",
      "2418:1203280\tQ-min: 1.254\tQ-max: 1.523\tLives: 5\tReward: 3.0\tEpisode Mean: 19.9\n",
      "2418:1203315\tQ-min: 1.270\tQ-max: 1.542\tLives: 5\tReward: 4.0\tEpisode Mean: 19.9\n",
      "2418:1203336\tQ-min: 0.185\tQ-max: 0.211\tLives: 4\tReward: 4.0\tEpisode Mean: 19.9\n",
      "2418:1203391\tQ-min: 1.239\tQ-max: 1.317\tLives: 4\tReward: 5.0\tEpisode Mean: 19.9\n",
      "2418:1203454\tQ-min: 1.245\tQ-max: 1.396\tLives: 4\tReward: 6.0\tEpisode Mean: 19.9\n",
      "2418:1203506\tQ-min: 1.200\tQ-max: 1.544\tLives: 4\tReward: 7.0\tEpisode Mean: 19.9\n",
      "2418:1203542\tQ-min: 1.266\tQ-max: 1.521\tLives: 4\tReward: 8.0\tEpisode Mean: 19.9\n",
      "2418:1203574\tQ-min: 1.254\tQ-max: 1.423\tLives: 4\tReward: 9.0\tEpisode Mean: 19.9\n",
      "2418:1203595\tQ-min: 0.044\tQ-max: 0.101\tLives: 3\tReward: 9.0\tEpisode Mean: 19.9\n",
      "2418:1203642\tQ-min: 1.226\tQ-max: 1.492\tLives: 3\tReward: 10.0\tEpisode Mean: 19.9\n",
      "2418:1203694\tQ-min: 1.216\tQ-max: 1.455\tLives: 3\tReward: 11.0\tEpisode Mean: 19.9\n",
      "2418:1203758\tQ-min: 1.244\tQ-max: 1.420\tLives: 3\tReward: 12.0\tEpisode Mean: 19.9\n",
      "2418:1203804\tQ-min: 1.262\tQ-max: 1.491\tLives: 3\tReward: 13.0\tEpisode Mean: 19.9\n",
      "2418:1203838\tQ-min: 1.243\tQ-max: 1.547\tLives: 3\tReward: 17.0\tEpisode Mean: 19.9\n",
      "2418:1203870\tQ-min: 1.232\tQ-max: 1.505\tLives: 3\tReward: 18.0\tEpisode Mean: 19.9\n",
      "2418:1203905\tQ-min: 1.256\tQ-max: 1.547\tLives: 3\tReward: 22.0\tEpisode Mean: 19.9\n",
      "2418:1203956\tQ-min: 1.249\tQ-max: 1.360\tLives: 3\tReward: 23.0\tEpisode Mean: 19.9\n",
      "2418:1204020\tQ-min: 1.286\tQ-max: 1.434\tLives: 3\tReward: 24.0\tEpisode Mean: 19.9\n",
      "2418:1204089\tQ-min: 1.012\tQ-max: 1.337\tLives: 3\tReward: 28.0\tEpisode Mean: 19.9\n",
      "2418:1204158\tQ-min: 1.232\tQ-max: 1.487\tLives: 3\tReward: 29.0\tEpisode Mean: 19.9\n",
      "2418:1204203\tQ-min: 0.071\tQ-max: 0.110\tLives: 2\tReward: 29.0\tEpisode Mean: 19.9\n",
      "2418:1204245\tQ-min: 1.223\tQ-max: 1.565\tLives: 2\tReward: 30.0\tEpisode Mean: 19.9\n",
      "2418:1204287\tQ-min: 1.197\tQ-max: 1.572\tLives: 2\tReward: 31.0\tEpisode Mean: 19.9\n",
      "2418:1204344\tQ-min: 1.209\tQ-max: 1.404\tLives: 2\tReward: 32.0\tEpisode Mean: 19.9\n",
      "2418:1204387\tQ-min: 0.107\tQ-max: 0.137\tLives: 1\tReward: 32.0\tEpisode Mean: 19.9\n",
      "2418:1204441\tQ-min: 1.249\tQ-max: 1.339\tLives: 1\tReward: 33.0\tEpisode Mean: 19.9\n",
      "2418:1204506\tQ-min: 1.267\tQ-max: 1.347\tLives: 1\tReward: 34.0\tEpisode Mean: 19.9\n",
      "2418:1204576\tQ-min: 1.068\tQ-max: 1.384\tLives: 1\tReward: 38.0\tEpisode Mean: 19.9\n",
      "2418:1204628\tQ-min: 1.240\tQ-max: 1.613\tLives: 1\tReward: 39.0\tEpisode Mean: 19.9\n",
      "2418:1204661\tQ-min: 1.260\tQ-max: 1.551\tLives: 1\tReward: 40.0\tEpisode Mean: 19.9\n",
      "2418:1204682\tQ-min: 0.011\tQ-max: 0.089\tLives: 0\tReward: 40.0\tEpisode Mean: 20.6\n",
      "2419:1204735\tQ-min: 1.220\tQ-max: 1.389\tLives: 5\tReward: 1.0\tEpisode Mean: 20.6\n",
      "2419:1204777\tQ-min: 0.086\tQ-max: 0.122\tLives: 4\tReward: 1.0\tEpisode Mean: 20.6\n",
      "2419:1204832\tQ-min: 1.245\tQ-max: 1.367\tLives: 4\tReward: 2.0\tEpisode Mean: 20.6\n",
      "2419:1204893\tQ-min: 1.230\tQ-max: 1.409\tLives: 4\tReward: 3.0\tEpisode Mean: 20.6\n",
      "2419:1204949\tQ-min: 1.239\tQ-max: 1.381\tLives: 4\tReward: 4.0\tEpisode Mean: 20.6\n",
      "2419:1204982\tQ-min: 1.258\tQ-max: 1.478\tLives: 4\tReward: 5.0\tEpisode Mean: 20.6\n",
      "2419:1205014\tQ-min: 1.278\tQ-max: 1.550\tLives: 4\tReward: 6.0\tEpisode Mean: 20.6\n",
      "2419:1205047\tQ-min: 1.207\tQ-max: 1.475\tLives: 4\tReward: 7.0\tEpisode Mean: 20.6\n",
      "2419:1205067\tQ-min: 0.093\tQ-max: 0.138\tLives: 3\tReward: 7.0\tEpisode Mean: 20.6\n",
      "2419:1205108\tQ-min: 1.248\tQ-max: 1.406\tLives: 3\tReward: 8.0\tEpisode Mean: 20.6\n",
      "2419:1205161\tQ-min: 1.235\tQ-max: 1.389\tLives: 3\tReward: 9.0\tEpisode Mean: 20.6\n",
      "2419:1205202\tQ-min: 0.086\tQ-max: 0.129\tLives: 2\tReward: 9.0\tEpisode Mean: 20.6\n",
      "2419:1205249\tQ-min: 0.806\tQ-max: 1.165\tLives: 2\tReward: 13.0\tEpisode Mean: 20.6\n",
      "2419:1205298\tQ-min: 0.887\tQ-max: 1.359\tLives: 2\tReward: 17.0\tEpisode Mean: 20.6\n",
      "2419:1205312\tQ-min: 0.120\tQ-max: 0.178\tLives: 1\tReward: 17.0\tEpisode Mean: 20.6\n",
      "2419:1205345\tQ-min: 0.082\tQ-max: 0.122\tLives: 0\tReward: 17.0\tEpisode Mean: 20.5\n",
      "2420:1205389\tQ-min: 1.237\tQ-max: 1.476\tLives: 5\tReward: 1.0\tEpisode Mean: 20.5\n",
      "2420:1205418\tQ-min: 0.073\tQ-max: 0.118\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n",
      "2420:1205469\tQ-min: 1.249\tQ-max: 1.324\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2420:1205522\tQ-min: 1.275\tQ-max: 1.439\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n",
      "2420:1205575\tQ-min: 1.333\tQ-max: 1.425\tLives: 4\tReward: 4.0\tEpisode Mean: 20.5\n",
      "2420:1205617\tQ-min: 0.101\tQ-max: 0.136\tLives: 3\tReward: 4.0\tEpisode Mean: 20.5\n",
      "2420:1205672\tQ-min: 1.256\tQ-max: 1.338\tLives: 3\tReward: 5.0\tEpisode Mean: 20.5\n",
      "2420:1205732\tQ-min: 1.245\tQ-max: 1.397\tLives: 3\tReward: 6.0\tEpisode Mean: 20.5\n",
      "2420:1205784\tQ-min: 1.299\tQ-max: 1.454\tLives: 3\tReward: 7.0\tEpisode Mean: 20.5\n",
      "2420:1205820\tQ-min: 1.227\tQ-max: 1.574\tLives: 3\tReward: 8.0\tEpisode Mean: 20.5\n",
      "2420:1205852\tQ-min: 1.246\tQ-max: 1.460\tLives: 3\tReward: 9.0\tEpisode Mean: 20.5\n",
      "2420:1205887\tQ-min: 1.365\tQ-max: 1.435\tLives: 3\tReward: 10.0\tEpisode Mean: 20.5\n",
      "2420:1205909\tQ-min: 0.131\tQ-max: 0.145\tLives: 2\tReward: 10.0\tEpisode Mean: 20.5\n",
      "2420:1205954\tQ-min: 1.252\tQ-max: 1.531\tLives: 2\tReward: 11.0\tEpisode Mean: 20.5\n",
      "2420:1205985\tQ-min: 0.097\tQ-max: 0.133\tLives: 1\tReward: 11.0\tEpisode Mean: 20.5\n",
      "2420:1206029\tQ-min: 1.203\tQ-max: 1.463\tLives: 1\tReward: 15.0\tEpisode Mean: 20.5\n",
      "2420:1206076\tQ-min: 0.830\tQ-max: 1.096\tLives: 1\tReward: 19.0\tEpisode Mean: 20.5\n",
      "2420:1206098\tQ-min: 1.233\tQ-max: 1.522\tLives: 1\tReward: 20.0\tEpisode Mean: 20.5\n",
      "2420:1206112\tQ-min: 0.140\tQ-max: 0.153\tLives: 0\tReward: 20.0\tEpisode Mean: 20.5\n",
      "2421:1206156\tQ-min: 1.244\tQ-max: 1.475\tLives: 5\tReward: 1.0\tEpisode Mean: 20.5\n",
      "2421:1206183\tQ-min: 0.072\tQ-max: 0.114\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n",
      "2421:1206238\tQ-min: 1.167\tQ-max: 1.451\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n",
      "2421:1206303\tQ-min: 1.246\tQ-max: 1.382\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n",
      "2421:1206357\tQ-min: 1.233\tQ-max: 1.420\tLives: 4\tReward: 4.0\tEpisode Mean: 20.5\n",
      "2421:1206395\tQ-min: 1.247\tQ-max: 1.588\tLives: 4\tReward: 5.0\tEpisode Mean: 20.5\n",
      "2421:1206425\tQ-min: 1.263\tQ-max: 1.477\tLives: 4\tReward: 6.0\tEpisode Mean: 20.5\n",
      "2421:1206459\tQ-min: 1.332\tQ-max: 1.578\tLives: 4\tReward: 7.0\tEpisode Mean: 20.5\n",
      "2421:1206492\tQ-min: 1.199\tQ-max: 1.486\tLives: 4\tReward: 11.0\tEpisode Mean: 20.5\n",
      "2421:1206541\tQ-min: 1.196\tQ-max: 1.424\tLives: 4\tReward: 12.0\tEpisode Mean: 20.5\n",
      "2421:1206583\tQ-min: 0.097\tQ-max: 0.121\tLives: 3\tReward: 12.0\tEpisode Mean: 20.5\n",
      "2421:1206625\tQ-min: 1.214\tQ-max: 1.495\tLives: 3\tReward: 13.0\tEpisode Mean: 20.5\n",
      "2421:1206670\tQ-min: 1.292\tQ-max: 1.393\tLives: 3\tReward: 14.0\tEpisode Mean: 20.5\n",
      "2421:1206730\tQ-min: 1.136\tQ-max: 1.544\tLives: 3\tReward: 18.0\tEpisode Mean: 20.5\n",
      "2421:1206783\tQ-min: 1.237\tQ-max: 1.514\tLives: 3\tReward: 19.0\tEpisode Mean: 20.5\n",
      "2421:1206817\tQ-min: 1.265\tQ-max: 1.514\tLives: 3\tReward: 20.0\tEpisode Mean: 20.5\n",
      "2421:1206853\tQ-min: 1.172\tQ-max: 1.514\tLives: 3\tReward: 24.0\tEpisode Mean: 20.5\n",
      "2421:1206875\tQ-min: 0.094\tQ-max: 0.119\tLives: 2\tReward: 24.0\tEpisode Mean: 20.5\n",
      "2421:1206925\tQ-min: 0.921\tQ-max: 1.427\tLives: 2\tReward: 28.0\tEpisode Mean: 20.5\n",
      "2421:1206940\tQ-min: 0.208\tQ-max: 0.244\tLives: 1\tReward: 28.0\tEpisode Mean: 20.5\n",
      "2421:1206992\tQ-min: 0.711\tQ-max: 1.110\tLives: 1\tReward: 32.0\tEpisode Mean: 20.5\n",
      "2421:1207005\tQ-min: 0.121\tQ-max: 0.141\tLives: 0\tReward: 32.0\tEpisode Mean: 20.9\n"
     ]
    }
   ],
   "source": [
    "agent.run(num_episodes=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now print some statistics for the episode rewards, which vary greatly from one episode to the next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rewards for 30 episodes:\n",
      "- Min:    8.0\n",
      "- Mean:   20.866666666666667\n",
      "- Max:    40.0\n",
      "- Stdev:  8.155706931686273\n"
     ]
    }
   ],
   "source": [
    "rewards = agent.episode_rewards\n",
    "print(\"Rewards for {0} episodes:\".format(len(rewards)))\n",
    "print(\"- Min:   \", np.min(rewards))\n",
    "print(\"- Mean:  \", np.mean(rewards))\n",
    "print(\"- Max:   \", np.max(rewards))\n",
    "print(\"- Stdev: \", np.std(rewards))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a histogram with the episode rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAQsElEQVR4nO3df6zddX3H8efLUtFEI9PeKGl7vW6QLWoE9A4x7A+CIalCYIu4QKaC0XQzEjFxc+AfOEmW4B8TpxhJB4zqiELQuAoYQwSj/mH1FgsC1aw6DCWdLb8lKqb63h/ni7s7nnPPubfn9t776fORnPT749PzffXT9tVvv+ec70lVIUla+5630gEkSZNhoUtSIyx0SWqEhS5JjbDQJakRx6zUgTds2FAzMzMrdXhJWpN27dr1aFVNDdq3YoU+MzPD3NzcSh1ektakJD8bts9LLpLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRYxd6knVJfpDktgH7jk1yc5K9SXYmmZlkSEnSaIs5Q78U2DNk33uAJ6rqBOBq4OOHG0yStDhjFXqSTcDZwHVDhpwHbO+WbwXenCSHH0+SNK5xPyn6SeDDwIuH7N8IPAxQVYeSPAW8DHh0/qAkW4GtANPT00vJqwbMXHb7WOMeuursZU4itWXkGXqSc4ADVbXrcA9WVduqaraqZqemBt6KQJK0RONccjkdODfJQ8AXgTOT/EffmEeAzQBJjgFeAjw2wZySpBFGFnpVXV5Vm6pqBrgAuKuq3tE3bAdwUbd8fjfGLyuVpCNoyXdbTHIlMFdVO4Drgc8n2Qs8Tq/4JUlH0KIKvaq+CXyzW75i3vZfA2+fZDBJ0uL4SVFJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiPG+ZLoFyT5XpJ7kzyQ5GMDxlyc5GCS3d3jvcsTV5I0zDjfWPQscGZVPZNkPfCdJF+rqu/2jbu5qi6ZfERJ0jhGFnr3Zc/PdKvru4dfAC1Jq8xY19CTrEuyGzgA3FlVOwcMe1uS+5LcmmTzRFNKkkYaq9Cr6rdVdTKwCTg1yWv7hnwVmKmq1wF3AtsHPU+SrUnmkswdPHjwcHJLkvos6l0uVfUkcDewpW/7Y1X1bLd6HfCGIT9/W1XNVtXs1NTUUvJKkoYY510uU0mO65ZfCJwF/KhvzPHzVs8F9kwypCRptHHe5XI8sD3JOnr/ANxSVbcluRKYq6odwAeSnAscAh4HLl6uwJKkwcZ5l8t9wCkDtl8xb/ly4PLJRpMkLYafFJWkRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGjPOdoi9I8r0k9yZ5IMnHBow5NsnNSfYm2ZlkZjnCSpKGG+cM/VngzKo6CTgZ2JLktL4x7wGeqKoTgKuBj082piRplJGFXj3PdKvru0f1DTsP2N4t3wq8OUkmllKSNNLIL4kGSLIO2AWcAHymqnb2DdkIPAxQVYeSPAW8DHi073m2AlsBpqenDy/5Kjdz2e1jjXvoqrMn+nyLMe6xJa0NY70oWlW/raqTgU3AqUleu5SDVdW2qpqtqtmpqamlPIUkaYhFvculqp4E7ga29O16BNgMkOQY4CXAY5MIKEkazzjvcplKcly3/ELgLOBHfcN2ABd1y+cDd1VV/3V2SdIyGuca+vHA9u46+vOAW6rqtiRXAnNVtQO4Hvh8kr3A48AFy5ZYkjTQyEKvqvuAUwZsv2Le8q+Bt082miRpMfykqCQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDVinO8U3Zzk7iQPJnkgyaUDxpyR5Kkku7vHFYOeS5K0fMb5TtFDwIeq6p4kLwZ2Jbmzqh7sG/ftqjpn8hElSeMYeYZeVfur6p5u+RfAHmDjcgeTJC3Ooq6hJ5mh94XROwfsflOSe5N8Lclrhvz8rUnmkswdPHhw0WElScONXehJXgR8CfhgVT3dt/se4JVVdRLwaeArg56jqrZV1WxVzU5NTS01syRpgLEKPcl6emV+U1V9uX9/VT1dVc90y3cA65NsmGhSSdKCxnmXS4DrgT1V9YkhY17RjSPJqd3zPjbJoJKkhY3zLpfTgXcCP0yyu9v2EWAaoKquBc4H3pfkEPAr4IKqqmXIK0kaYmShV9V3gIwYcw1wzaRCSZIWz0+KSlIjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiPG+U7RzUnuTvJgkgeSXDpgTJJ8KsneJPclef3yxJUkDTPOd4oeAj5UVfckeTGwK8mdVfXgvDFvAU7sHm8EPtv9KEk6QkaeoVfV/qq6p1v+BbAH2Ng37Dzgc9XzXeC4JMdPPK0kaahxztB/L8kMcAqws2/XRuDheev7um37+37+VmArwPT09OKSzjNz2e1jj33oqrOXfJzWjTuPzuHhc66PDiv9+zz2i6JJXgR8CfhgVT29lINV1baqmq2q2ampqaU8hSRpiLEKPcl6emV+U1V9ecCQR4DN89Y3ddskSUfIOO9yCXA9sKeqPjFk2A7gXd27XU4Dnqqq/UPGSpKWwTjX0E8H3gn8MMnubttHgGmAqroWuAN4K7AX+CXw7slHlSQtZGShV9V3gIwYU8D7JxVKkrR4flJUkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGjHOd4rekORAkvuH7D8jyVNJdnePKyYfU5I0yjjfKXojcA3wuQXGfLuqzplIIknSkow8Q6+qbwGPH4EskqTDMKlr6G9Kcm+SryV5zbBBSbYmmUsyd/DgwQkdWpIEkyn0e4BXVtVJwKeBrwwbWFXbqmq2qmanpqYmcGhJ0nMOu9Cr6umqeqZbvgNYn2TDYSeTJC3KYRd6klckSbd8avecjx3u80qSFmfku1ySfAE4A9iQZB/wUWA9QFVdC5wPvC/JIeBXwAVVVcuWWJI00MhCr6oLR+y/ht7bGiVJK8hPikpSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjRhZ6khuSHEhy/5D9SfKpJHuT3Jfk9ZOPKUkaZZwz9BuBLQvsfwtwYvfYCnz28GNJkhZrZKFX1beAxxcYch7wuer5LnBckuMnFVCSNJ6RXxI9ho3Aw/PW93Xb9vcPTLKV3lk809PTEzi0joSZy25v4rgPXXX2RJ8PJp9x3Oeb9K9lpX6PYXl+X45WR/RF0araVlWzVTU7NTV1JA8tSc2bRKE/Amyet76p2yZJOoImUeg7gHd173Y5DXiqqv7gcoskaXmNvIae5AvAGcCGJPuAjwLrAarqWuAO4K3AXuCXwLuXK6wkabiRhV5VF47YX8D7J5ZIkrQkflJUkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGjFWoSfZkuTHSfYmuWzA/ouTHEyyu3u8d/JRJUkLGec7RdcBnwHOAvYB30+yo6oe7Bt6c1VdsgwZJUljGOcM/VRgb1X9tKp+A3wROG95Y0mSFmucQt8IPDxvfV+3rd/bktyX5NYkmwc9UZKtSeaSzB08eHAJcSVJw0zqRdGvAjNV9TrgTmD7oEFVta2qZqtqdmpqakKHliTBeIX+CDD/jHtTt+33quqxqnq2W70OeMNk4kmSxjVOoX8fODHJq5I8H7gA2DF/QJLj562eC+yZXERJ0jhGvsulqg4luQT4OrAOuKGqHkhyJTBXVTuADyQ5FzgEPA5cvIyZJUkDjCx0gKq6A7ijb9sV85YvBy6fbDRJ0mL4SVFJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqxFiFnmRLkh8n2ZvksgH7j01yc7d/Z5KZSQeVJC1sZKEnWQd8BngL8GrgwiSv7hv2HuCJqjoBuBr4+KSDSpIWNs4Z+qnA3qr6aVX9BvgicF7fmPOA7d3yrcCbk2RyMSVJo6SqFh6QnA9sqar3duvvBN5YVZfMG3N/N2Zft/6Tbsyjfc+1Fdjarf4p8ONJ/UIOwwbg0ZGjVi/zr5y1nB3Wdv61nB0OL/8rq2pq0I5jlp5n8apqG7DtSB5zlCRzVTW70jmWyvwrZy1nh7Wdfy1nh+XLP84ll0eAzfPWN3XbBo5JcgzwEuCxSQSUJI1nnEL/PnBiklcleT5wAbCjb8wO4KJu+Xzgrhp1LUeSNFEjL7lU1aEklwBfB9YBN1TVA0muBOaqagdwPfD5JHuBx+mV/lqxqi4BLYH5V85azg5rO/9azg7LlH/ki6KSpLXBT4pKUiMsdElqxFFV6EluSHKge9/8c9temuTOJP/V/fhHK5lxIUPy/1OSR5Ls7h5vXcmMwyTZnOTuJA8meSDJpd32VT//C2RfK3P/giTfS3Jvl/9j3fZXdbfq2NvduuP5K511kAXy35jkv+fN/8krnXWYJOuS/CDJbd36ssz9UVXowI3Alr5tlwHfqKoTgW9066vVjfxhfoCrq+rk7nHHEc40rkPAh6rq1cBpwPu7W0ishfkflh3Wxtw/C5xZVScBJwNbkpxG7xYdV3e37HiC3i08VqNh+QH+Yd787165iCNdCuyZt74sc39UFXpVfYveu3Dmm3/bgu3AXx7RUIswJP+aUFX7q+qebvkX9P5wb2QNzP8C2deE6nmmW13fPQo4k96tOmCVzj0smH9NSLIJOBu4rlsPyzT3R1WhD/HyqtrfLf8P8PKVDLNElyS5r7sks+ouWfTr7sZ5CrCTNTb/fdlhjcx991/+3cAB4E7gJ8CTVXWoG7KPVfyPVH/+qnpu/v+5m/+rkxy7ghEX8kngw8DvuvWXsUxzb6HP030Yas38y9/5LPAn9P4ruh/4l5WNs7AkLwK+BHywqp6ev2+1z/+A7Gtm7qvqt1V1Mr1Pep8K/NkKR1qU/vxJXgtcTu/X8efAS4F/XMGIAyU5BzhQVbuOxPEsdPh5kuMBuh8PrHCeRamqn3d/2H8H/Bu9v6yrUpL19Arxpqr6crd5Tcz/oOxrae6fU1VPAncDbwKO627VAYNv6bHqzMu/pbsUVlX1LPDvrM75Px04N8lD9O5UeybwryzT3Fvo//+2BRcB/7mCWRbtuTLs/BVw/7CxK6m7bng9sKeqPjFv16qf/2HZ19DcTyU5rlt+IXAWvdcB7qZ3qw5YpXMPQ/P/aN6JQOhdg151819Vl1fVpqqaofcJ+ruq6m9Yprk/qj4pmuQLwBn0bl35c+CjwFeAW4Bp4GfAX1fVqnzhcUj+M+j9l7+Ah4C/nXdNetVI8hfAt4Ef8n/XEj9C71r0qp7/BbJfyNqY+9fRe+FtHb2TuFuq6sokf0zvrPGlwA+Ad3Rnu6vKAvnvAqaAALuBv5v34umqk+QM4O+r6pzlmvujqtAlqWVecpGkRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqRH/C0dYO3X9eg2gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(rewards, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example States\n",
    "\n",
    "We can plot examples of states from the game-environment and the Q-values that are estimated by the Neural Network.\n",
    "\n",
    "This helper-function prints the Q-values for a given index in the replay-memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_q_values(idx):\n",
    "    \"\"\"Print Q-values and actions from the replay-memory at the given index.\"\"\"\n",
    "\n",
    "    # Get the Q-values and action from the replay-memory.\n",
    "    q_values = replay_memory.q_values[idx]\n",
    "    action = replay_memory.actions[idx]\n",
    "\n",
    "    print(\"Action:     Q-Value:\")\n",
    "    print(\"====================\")\n",
    "\n",
    "    # Print all the actions and their Q-values.\n",
    "    for i, q_value in enumerate(q_values):\n",
    "        # Used to display which action was taken.\n",
    "        if i == action:\n",
    "            action_taken = \"(Action Taken)\"\n",
    "        else:\n",
    "            action_taken = \"\"\n",
    "\n",
    "        # Text-name of the action.\n",
    "        action_name = agent.get_action_name(i)\n",
    "            \n",
    "        print(\"{0:12}{1:.3f} {2}\".format(action_name, q_value,\n",
    "                                        action_taken))\n",
    "\n",
    "    # Newline.\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This helper-function plots a state from the replay-memory and optionally prints the Q-values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_state(idx, print_q=True):\n",
    "    \"\"\"Plot the state in the replay-memory with the given index.\"\"\"\n",
    "\n",
    "    # Get the state from the replay-memory.\n",
    "    state = replay_memory.states[idx]\n",
    "    \n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(1, 2)\n",
    "\n",
    "    # Plot the image from the game-environment.\n",
    "    ax = axes.flat[0]\n",
    "    ax.imshow(state[:, :, 0], vmin=0, vmax=255,\n",
    "              interpolation='lanczos', cmap='gray')\n",
    "\n",
    "    # Plot the motion-trace.\n",
    "    ax = axes.flat[1]\n",
    "    ax.imshow(state[:, :, 1], vmin=0, vmax=255,\n",
    "              interpolation='lanczos', cmap='gray')\n",
    "\n",
    "    # This is necessary if we show more than one plot in a single Notebook cell.\n",
    "    plt.show()\n",
    "    \n",
    "    # Print the Q-values.\n",
    "    if print_q:\n",
    "        print_q_values(idx=idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The replay-memory has room for 200k states but it is only partially full from the above call to `agent.run(num_episodes=1)`. This is how many states are actually used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1061"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_used = replay_memory.num_used\n",
    "num_used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the Q-values from the replay-memory that are actually used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_values = replay_memory.q_values[0:num_used, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each state, calculate the min / max Q-values and their difference. This will be used to lookup interesting states in the following sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_values_min = q_values.min(axis=1)\n",
    "q_values_max = q_values.max(axis=1)\n",
    "q_values_dif = q_values_max - q_values_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example States: Highest Reward\n",
    "\n",
    "This example shows the states surrounding the state with the highest reward.\n",
    "\n",
    "During the training we limit the rewards to the range [-1, 1] so this basically just gets the first state that has a reward of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmax(replay_memory.rewards)\n",
    "idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This state is where the ball hits the wall so the agent scores a point. \n",
    "\n",
    "We can show the surrounding states leading up to and following this state. Note how the Q-values are very close for the different actions, because at this point it really does not matter what the agent does as the reward is already guaranteed. But note how the Q-values decrease significantly after the ball has hit the wall and a point has been scored.\n",
    "\n",
    "Also note that the agent uses the Epsilon-greedy policy for taking actions, so there is a small probability that a random action is taken instead of the action with the highest Q-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.188 \n",
      "FIRE        1.169 \n",
      "RIGHT       1.148 \n",
      "LEFT        1.278 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.220 \n",
      "FIRE        1.206 \n",
      "RIGHT       1.163 \n",
      "LEFT        1.310 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.360 (Action Taken)\n",
      "FIRE        1.271 \n",
      "RIGHT       1.217 \n",
      "LEFT        1.274 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.301 \n",
      "FIRE        1.335 (Action Taken)\n",
      "RIGHT       1.243 \n",
      "LEFT        1.305 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.307 \n",
      "FIRE        1.337 \n",
      "RIGHT       1.255 \n",
      "LEFT        1.435 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.362 \n",
      "FIRE        1.359 \n",
      "RIGHT       1.260 \n",
      "LEFT        1.496 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.391 \n",
      "FIRE        0.377 \n",
      "RIGHT       0.366 (Action Taken)\n",
      "LEFT        0.430 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.394 \n",
      "FIRE        0.386 \n",
      "RIGHT       0.369 \n",
      "LEFT        0.436 (Action Taken)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(-5, 3):\n",
    "    plot_state(idx=idx+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Highest Q-Value\n",
    "\n",
    "This example shows the states surrounding the one with the highest Q-values. This means that the agent has high expectation that several points will be scored in the following steps. Note that the Q-values decrease significantly after the points have been scored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "517"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmax(q_values_max)\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.289 \n",
      "FIRE        1.206 \n",
      "RIGHT       1.333 \n",
      "LEFT        1.653 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        1.073 \n",
      "FIRE        1.088 \n",
      "RIGHT       1.106 \n",
      "LEFT        1.239 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.563 \n",
      "FIRE        0.589 \n",
      "RIGHT       0.629 (Action Taken)\n",
      "LEFT        0.553 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.506 \n",
      "FIRE        0.514 \n",
      "RIGHT       0.564 (Action Taken)\n",
      "LEFT        0.548 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.503 \n",
      "FIRE        0.513 \n",
      "RIGHT       0.559 (Action Taken)\n",
      "LEFT        0.520 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    plot_state(idx=idx+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Loss of Life\n",
    "\n",
    "This example shows the states leading up to a loss of life for the agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmax(replay_memory.end_life)\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.627 (Action Taken)\n",
      "FIRE        0.617 \n",
      "RIGHT       0.605 \n",
      "LEFT        0.585 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.585 \n",
      "FIRE        0.589 (Action Taken)\n",
      "RIGHT       0.566 \n",
      "LEFT        0.564 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.378 \n",
      "FIRE        0.380 (Action Taken)\n",
      "RIGHT       0.375 \n",
      "LEFT        0.360 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.225 \n",
      "FIRE        0.232 \n",
      "RIGHT       0.232 \n",
      "LEFT        0.236 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.197 \n",
      "FIRE        0.203 \n",
      "RIGHT       0.217 (Action Taken)\n",
      "LEFT        0.208 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.184 (Action Taken)\n",
      "FIRE        0.171 \n",
      "RIGHT       0.188 \n",
      "LEFT        0.183 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.187 \n",
      "FIRE        0.177 \n",
      "RIGHT       0.194 (Action Taken)\n",
      "LEFT        0.191 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.149 (Action Taken)\n",
      "FIRE        0.113 \n",
      "RIGHT       0.141 \n",
      "LEFT        0.126 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.140 (Action Taken)\n",
      "FIRE        0.108 \n",
      "RIGHT       0.132 \n",
      "LEFT        0.111 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.137 (Action Taken)\n",
      "FIRE        0.109 \n",
      "RIGHT       0.130 \n",
      "LEFT        0.105 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(-10, 0):\n",
    "    plot_state(idx=idx+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Greatest Difference in Q-Values\n",
    "\n",
    "This example shows the state where there is the greatest difference in Q-values, which means that the agent believes one action will be much more beneficial than another. But because the agent uses the Epsilon-greedy policy, it sometimes selects a random action instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmax(q_values_dif)\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.428 \n",
      "FIRE        0.428 \n",
      "RIGHT       0.906 (Action Taken)\n",
      "LEFT        0.358 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.391 \n",
      "FIRE        0.426 \n",
      "RIGHT       0.849 (Action Taken)\n",
      "LEFT        0.311 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.413 \n",
      "FIRE        0.486 \n",
      "RIGHT       0.852 (Action Taken)\n",
      "LEFT        0.306 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.409 \n",
      "FIRE        0.434 \n",
      "RIGHT       0.737 (Action Taken)\n",
      "LEFT        0.332 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.443 \n",
      "FIRE        0.692 (Action Taken)\n",
      "RIGHT       0.585 \n",
      "LEFT        0.308 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    plot_state(idx=idx+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Smallest Difference in Q-Values\n",
    "\n",
    "This example shows the state where there is the smallest difference in Q-values, which means that the agent believes it does not really matter which action it selects, as they all have roughly the same expectations for future rewards.\n",
    "\n",
    "The Neural Network estimates these Q-values and they are not precise. The differences in Q-values may be so small that they fall within the error-range of the estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmin(q_values_dif)\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.600 (Action Taken)\n",
      "FIRE        0.595 \n",
      "RIGHT       0.596 \n",
      "LEFT        0.599 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.572 \n",
      "FIRE        0.566 \n",
      "RIGHT       0.545 \n",
      "LEFT        0.704 (Action Taken)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.654 \n",
      "FIRE        0.674 \n",
      "RIGHT       0.663 (Action Taken)\n",
      "LEFT        0.615 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.655 (Action Taken)\n",
      "FIRE        0.646 \n",
      "RIGHT       0.719 \n",
      "LEFT        0.648 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action:     Q-Value:\n",
      "====================\n",
      "NOOP        0.686 (Action Taken)\n",
      "FIRE        0.660 \n",
      "RIGHT       0.675 \n",
      "LEFT        0.675 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    plot_state(idx=idx+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output of Convolutional Layers\n",
    "\n",
    "The outputs of the convolutional layers can be plotted so we can see how the images from the game-environment are being processed by the Neural Network.\n",
    "\n",
    "This is the helper-function for plotting the output of the convolutional layer with the given name, when inputting the given state from the replay-memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_layer_output(model, layer_name, state_index, inverse_cmap=False):\n",
    "    \"\"\"\n",
    "    Plot the output of a convolutional layer.\n",
    "\n",
    "    :param model: An instance of the NeuralNetwork-class.\n",
    "    :param layer_name: Name of the convolutional layer.\n",
    "    :param state_index: Index into the replay-memory for a state that\n",
    "                        will be input to the Neural Network.\n",
    "    :param inverse_cmap: Boolean whether to inverse the color-map.\n",
    "    \"\"\"\n",
    "\n",
    "    # Get the given state-array from the replay-memory.\n",
    "    state = replay_memory.states[state_index]\n",
    "    \n",
    "    # Get the output tensor for the given layer inside the TensorFlow graph.\n",
    "    # This is not the value-contents but merely a reference to the tensor.\n",
    "    layer_tensor = model.get_layer_tensor(layer_name=layer_name)\n",
    "    \n",
    "    # Get the actual value of the tensor by feeding the state-data\n",
    "    # to the TensorFlow graph and calculating the value of the tensor.\n",
    "    values = model.get_tensor_value(tensor=layer_tensor, state=state)\n",
    "\n",
    "    # Number of image channels output by the convolutional layer.\n",
    "    num_images = values.shape[3]\n",
    "\n",
    "    # Number of grid-cells to plot.\n",
    "    # Rounded-up, square-root of the number of filters.\n",
    "    num_grids = math.ceil(math.sqrt(num_images))\n",
    "\n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(num_grids, num_grids, figsize=(10, 10))\n",
    "\n",
    "    print(\"Dim. of each image:\", values.shape)\n",
    "    \n",
    "    if inverse_cmap:\n",
    "        cmap = 'gray_r'\n",
    "    else:\n",
    "        cmap = 'gray'\n",
    "\n",
    "    # Plot the outputs of all the channels in the conv-layer.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the valid image-channels.\n",
    "        if i < num_images:\n",
    "            # Get the image for the i'th output channel.\n",
    "            img = values[0, :, :, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, interpolation='nearest', cmap=cmap)\n",
    "\n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "\n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Game State\n",
    "\n",
    "This is the state that is being input to the Neural Network. The image on the left is the last image from the game-environment. The image on the right is the processed motion-trace that shows the trajectories of objects in the game-environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = np.argmax(q_values_max)\n",
    "plot_state(idx=idx, print_q=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output of Convolutional Layer 1\n",
    "\n",
    "This shows the images that are output by the 1st convolutional layer, when inputting the above state to the Neural Network. There are 16 output channels of this convolutional layer.\n",
    "\n",
    "Note that you can invert the colors by setting `inverse_cmap=True` in the parameters to this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dim. of each image: (1, 53, 40, 16)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_layer_output(model=model, layer_name='layer_conv1', state_index=idx, inverse_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output of Convolutional Layer 2\n",
    "\n",
    "These are the images output by the 2nd convolutional layer, when inputting the above state to the Neural Network. There are 32 output channels of this convolutional layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dim. of each image: (1, 27, 20, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_layer_output(model=model, layer_name='layer_conv2', state_index=idx, inverse_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output of Convolutional Layer 3\n",
    "\n",
    "These are the images output by the 3rd convolutional layer, when inputting the above state to the Neural Network. There are 64 output channels of this convolutional layer.\n",
    "\n",
    "All these images are flattened to a one-dimensional array (or tensor) which is then used as the input to a fully-connected layer in the Neural Network.\n",
    "\n",
    "During the training-process, the Neural Network has learnt what convolutional filters to apply to the images from the game-environment so as to produce these images, because they have proven to be useful when estimating Q-values.\n",
    "\n",
    "Can you see what it is that the Neural Network has learned to detect in these images?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dim. of each image: (1, 27, 20, 64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_layer_output(model=model, layer_name='layer_conv3', state_index=idx, inverse_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nv2JqNLBhy1j"
   },
   "source": [
    "## Weights for Convolutional Layers\n",
    "\n",
    "We can also plot the weights of the convolutional layers in the Neural Network. These are the weights that are being optimized so as to improve the ability of the Neural Network to estimate Q-values. Tutorial #02 explains in greater detail what convolutional weights are.\n",
    "There are also weights for the fully-connected layers but they are not shown here.\n",
    "\n",
    "This is the helper-function for plotting the weights of a convoluational layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_conv_weights(model, layer_name, input_channel=0):\n",
    "    \"\"\"\n",
    "    Plot the weights for a convolutional layer.\n",
    "    \n",
    "    :param model: An instance of the NeuralNetwork-class.\n",
    "    :param layer_name: Name of the convolutional layer.\n",
    "    :param input_channel: Plot the weights for this input-channel.\n",
    "    \"\"\"\n",
    "\n",
    "    # Get the variable for the weights of the given layer.\n",
    "    # This is a reference to the variable inside TensorFlow,\n",
    "    # not its actual value.\n",
    "    weights_variable = model.get_weights_variable(layer_name=layer_name)\n",
    "    \n",
    "    # Retrieve the values of the weight-variable from TensorFlow.\n",
    "    # The format of this 4-dim tensor is determined by the\n",
    "    # TensorFlow API. See Tutorial #02 for more details.\n",
    "    w = model.get_variable_value(variable=weights_variable)\n",
    "\n",
    "    # Get the weights for the given input-channel.\n",
    "    w_channel = w[:, :, input_channel, :]\n",
    "    \n",
    "    # Number of output-channels for the conv. layer.\n",
    "    num_output_channels = w_channel.shape[2]\n",
    "\n",
    "    # Get the lowest and highest values for the weights.\n",
    "    # This is used to correct the colour intensity across\n",
    "    # the images so they can be compared with each other.\n",
    "    w_min = np.min(w_channel)\n",
    "    w_max = np.max(w_channel)\n",
    "\n",
    "    # This is used to center the colour intensity at zero.\n",
    "    abs_max = max(abs(w_min), abs(w_max))\n",
    "\n",
    "    # Print statistics for the weights.\n",
    "    print(\"Min:  {0:.5f}, Max:   {1:.5f}\".format(w_min, w_max))\n",
    "    print(\"Mean: {0:.5f}, Stdev: {1:.5f}\".format(w_channel.mean(),\n",
    "                                                 w_channel.std()))\n",
    "\n",
    "    # Number of grids to plot.\n",
    "    # Rounded-up, square-root of the number of output-channels.\n",
    "    num_grids = math.ceil(math.sqrt(num_output_channels))\n",
    "\n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "\n",
    "    # Plot all the filter-weights.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the valid filter-weights.\n",
    "        if i < num_output_channels:\n",
    "            # Get the weights for the i'th filter of this input-channel.\n",
    "            img = w_channel[:, :, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, vmin=-abs_max, vmax=abs_max,\n",
    "                      interpolation='nearest', cmap='seismic')\n",
    "\n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "\n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights for Convolutional Layer 1\n",
    "\n",
    "These are the weights of the first convolutional layer of the Neural Network, with respect to the first input channel of the state. That is, these are the weights that are used on the image from the game-environment. Some basic statistics are also shown.\n",
    "\n",
    "Note how the weights are more negative (blue) than positive (red). It is unclear why this happens as these weights are found through optimization. It is apparently beneficial for the following layers to have this processing with more negative weights in the first convolutional layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  -0.24494, Max:   0.13658\n",
      "Mean: -0.01729, Stdev: 0.06023\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(model=model, layer_name='layer_conv1', input_channel=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the convolutional weights for the second input channel, that is, the motion-trace of the game-environment. Once again we see that the negative weights (blue) have a much greater magnitude than the positive weights (red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  -0.56904, Max:   0.06957\n",
      "Mean: -0.05132, Stdev: 0.12694\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(model=model, layer_name='layer_conv1', input_channel=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights for Convolutional Layer 2\n",
    "\n",
    "These are the weights of the 2nd convolutional layer in the Neural Network. There are 16 input channels and 32 output channels of this layer. You can change the number for the input-channel to see the associated weights.\n",
    "\n",
    "Note how the weights are more balanced between positive (red) and negative (blue) compared to the weights for the 1st convolutional layer above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  -0.24590, Max:   0.14826\n",
      "Mean: -0.00605, Stdev: 0.06365\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(model=model, layer_name='layer_conv2', input_channel=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights for Convolutional Layer 3\n",
    "\n",
    "These are the weights of the 3rd convolutional layer in the Neural Network. There are 32 input channels and 64 output channels of this layer. You can change the number for the input-channel to see the associated weights.\n",
    "\n",
    "Note again how the weights are more balanced between positive (red) and negative (blue) compared to the weights for the 1st convolutional layer above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  -0.25325, Max:   0.17733\n",
      "Mean: -0.03257, Stdev: 0.07194\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(model=model, layer_name='layer_conv3', input_channel=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "We trained an agent to play old Atari games quite well using Reinforcement Learning. Recent improvements to the training algorithm have improved the performance significantly. But is this true human-like intelligence? The answer is clearly NO!\n",
    "\n",
    "Reinforcement Learning in its current form is a crude numerical algorithm for connecting visual images, actions, rewards and penalties when there is a time-lag between the signals. The learning is based on trial-and-error and cannot do logical reasoning like a human. The agent has no sense of \"self\" while a human has an understanding of what part of the game-environment it is controlling, so a human can reason logically like this: \"(A) I control the paddle, and (B) I must avoid dying which happens when the ball flies past the paddle, so (C) I must move the paddle to hit the ball, and (D) this automatically scores points when the ball smashes bricks in the wall\". A human would first learn these basic logical rules of the game - and then try and refine the eye-hand coordination to play the game better. Reinforcement Learning has no real comprehension of what is going on in the game and merely works on improving the eye-hand coordination until it gets lucky and does the right thing to score more points.\n",
    "\n",
    "Furthermore, the training of the Reinforcement Learning algorithm required almost 150 hours of computation which played the game at high speeds. If the game was played at normal real-time speeds then it would have taken more than 1700 hours to train the agent, which is more than 70 days and nights.\n",
    "\n",
    "Logical reasoning would allow for much faster learning than Reinforcement Learning, and it would be able to solve much more complicated problems than simple eye-hand coordination. I am skeptical if someone will be able to create true human-like intelligence from Reinforcement Learning algorithms.\n",
    "\n",
    "Does that mean Reinforcement Learning is completely worthless? No, it has real-world applications that currently cannot be solved by other methods.\n",
    "\n",
    "Another point of criticism is the use of Neural Networks. The majority of the research in Reinforcement Learning is actually spent on trying to stabilize the training of the Neural Network using various tricks. This is a waste of research time and strongly indicates that Neural Networks may not be a very good Machine Learning model compared to the human brain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises & Research Ideas\n",
    "\n",
    "Below are suggestions for exercises and experiments that may help improve your skills with TensorFlow and Reinforcement Learning. Some of these ideas can easily be extended into full research problems that would help the community if you can solve them.\n",
    "\n",
    "You should keep a log of your experiments, describing for each experiment the settings you tried and the results. You should also save the source-code and checkpoints / log-files.\n",
    "\n",
    "It takes so much time to run these experiments, so please share your results with the rest of the community. Even if an experiment failed to produce anything useful, it will be helpful to others so they know not to redo the same experiment.\n",
    "\n",
    "[Thread on GitHub for discussing these experiments](https://github.com/Hvass-Labs/TensorFlow-Tutorials/issues/32)\n",
    "\n",
    "You may want to backup this Notebook and the other files before making any changes.\n",
    "\n",
    "You may find it helpful to add more command-line parameters to `reinforcement_learning.py` so you don't have to edit the source-code for testing other parameters.\n",
    "\n",
    "* Change the epsilon-probability during testing to e.g. 0.001 or 0.05. Which gives the best results? Could you use this value during training? Why/not?\n",
    "* Try and change the game-environment to Space Invaders and re-run this Notebook. The hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n",
    "* Try different architectures for the Neural Network. You will need to restart the training because the checkpoints cannot be reused for other architectures. You will need to train the agent for several days with each new architecture so as to properly assess its performance.\n",
    "* The replay-memory throws away all data after optimization of the Neural Network. Can you make it reuse the data somehow? The ReplayMemory-class has the function `estimate_all_q_values()` which may be helpful.\n",
    "* The reward is limited to -1 and 1 in the function `ReplayMemory.add()` so as to stabilize the training. This means the agent cannot distinguish between small and large rewards. Can you use batch normalization to fix this problem, so you can use the actual reward values?\n",
    "* Can you improve the training by adding L2-regularization or dropout?\n",
    "* Try using other optimizers for the Neural Network. Does it help with the training speed or stability?\n",
    "* Let the agent take up to 30 random actions at the beginning of each new episode. This is used in some research papers to further randomize the game-environment, so the agent cannot memorize the first sequence of actions.\n",
    "* Try and save the game at regular intervals. If the agent dies, then you can reload the last saved game. Would this help training the agent faster and better, because it does not need to play the game from the beginning?\n",
    "* There are some invalid actions available to the agent in OpenAI Gym. Does it improve the training if you only allow the valid actions from the game-environment?\n",
    "* Does the MotionTracer work for other games? Can you improve on the MotionTracer?\n",
    "* Try and use the last 4 image-frames from the game instead of the MotionTracer.\n",
    "* Try larger and smaller sizes for the replay memory.\n",
    "* Try larger and smaller discount rates for updating the Q-values.\n",
    "* If you look closely in the states and actions that are display above, you will note that the agent has sometimes taken actions that do not correspond to the movement of the paddle. For example, the action might be LEFT but the paddle has either not moved at all, or it has moved right instead. Is this a bug in the source-code for this tutorial, or is it a bug in OpenAI Gym, or is it a bug in the underlying Atari Learning Environment? Does it matter?"
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    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
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