Deep RL - Continuous Control

• Organization: Udacity
• Course: Deep Reinforcement Learning NanoDegree.
• Project: #2 - Continuous Control

Project Details

For this project, we will work with the Reacher environment.

In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of our agent is to maintain its position at the target location for as many time steps as possible.

The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.

For this project, we will work with a Unity environment. It contains 20 identical agents, each with its own copy of the environment.

Solving the Environment

For solving the environment our agents must get an average score of +30 (over 100 consecutive episodes, and over all agents). Specifically,

• After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 20 (potentially different) scores. We then take the average of these 20 scores.
• This yields an average score for each episode (where the average is over all 20 agents).

The environment is considered solved, when the average (over 100 episodes) of those average scores is at least +30.

Getting Started

1. Clone this repository

2. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • Version 1: One (1) Agent

     • Linux: click here
     • Mac OSX: click here
     • Windows (32-bit): click here
     • Windows (64-bit): click here

   • Version 2: Twenty (20) Agents

     • Linux: click here
     • Mac OSX: click here
     • Windows (32-bit): click here
     • Windows (64-bit): click here

   (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

3. Place the file in the root folder of the cloned repository, and unzip (or decompress) the file.

Instructions

Code

UdacityDRLContinuousControl/
  |- .gitignore
  |- agent.py
  |- checkpoint_actor.pth
  |- checkpoint_critic.pth
  |- ddpg.py
  |- model.py
  |- README.md
  |- REPORT.md
  |- test.py
  |- train.py
  |- train_scores.png

• .gitignore - Gitignore file
• agent.py - Navigation agent, interacts with and learns from the environment.
• checkpoint_actor.pth - Trained model's state
• checkpoint_critic.pth - Trained model's state
• ddpg.py - Deep Deterministic Policy Gradient (train and test code)
• model.py - Actor and critic models
• README.md - this readme file
• REPORT.md - Implementation description
• test.py - Test the agent
• train.py - Train the agent
• train_scores.png - Training progress plot

Train

$ python train.py

Test

$ python test.py