WARNING: This is currently work-in-progress.

Reinforcement Learning in Swift

This repository contains a reinforcement learning library built using Swift for TensorFlow, that also encompasses the functionality of OpenAI Gym. The following is a list of currently supported features.

• All algorithms and interfaces are designed and implemented with batching in mind to support efficient training of neural networks that often operate on batched inputs.
• Environments:
  • Cart-Pole (classic control example)
  • Atari Games (using the Arcade Learning Environment)
  • Retro Games (atari, sega, etc., using Gym Retro)
• Agents:
  • Policy Gradient Algorithms:
    • REINFORCE
    • Advantage Actor Critic (A2C)
    • Proximal Policy Optimization (PPO)
    • UPCOMING: Deep Deterministic Policy Gradients (DDPG)
    • UPCOMING: Twin Delayed Deep Deterministic Policy Gradients (TD3)
    • UPCOMING: Soft Actor Critic (SAC)
  • Q-Learning Algorithms:
    • Deep Q-Networks (DQN)
    • UPCOMING: Double Deep Q-Networks (DDQN)
• Advantage Estimation Methods:
  • Empirical Advantage Estimation
  • Generalized Advantage Estimation (GAE)
• Replay Buffers:
  • Uniform Replay Buffer
  • UPCOMING: Prioritized Replay Buffer
• Visualization using OpenGL for all of the currently implemented environments.

Installation

Prerequisites

GLFW

GLFW is used for rendering. You can install it using:

# For MacOS:
brew install --HEAD git glfw3

# For Linux:
sudo apt install libglfw3-dev libglfw3

NOTE: The Swift Package Manager uses pkg-config to locate the installed libraries and so you need to make sure that pkg-config is configured correctly. That may require you to set the PKG_CONFIG_PATH environment variable correctly.

NOTE: If the rendered image does not update according to the specified frames per second value and you are using MacOS 10.14, you should update to 10.14.4 because there is a bug in previous releases of 10.14 which breaks VSync.

Reinforcement Learning Library Design Notes

WARNING: The below is not relevant anymore. I have been working on a new simpler and more powerful interface and plan to update the examples shown in this file soon.

Batching

Batching can occur at two levels:

• Environment:
• Policy:

For example, in the case of retro games, the environment can only operate on one action at a time (i.e., it is not batched). If we have a policy that is also not batched, then we the process of collecting trajectories for training looks as follows:

... → Policy → Environment → Policy → Environment → ...

In this diagram, the policy is invoked to produce the next action and then the environment is invoked to take a step using that action and return rewards, etc. If instead we are using a policy that can be batched (e.g., a convolutional neural network policy would be much more efficient if executed in a batched manner), then we can collect trajectories for training in the following manner:

               ↗ Environment ↘            ↗ Environment ↘
... ⇒ Policy ⇒ → Environment → ⇒ Policy ⇒ → Environment → ...
               ↘ Environment ↗            ↘ Environment ↗

where multiple copies of the environment are running separately, producing rewards that are then batched and fed all together to a single batched policy. This policy then produces a batch of actions that is split up and each action is in term fed to its corresponding environment. Similarly, we can have a batched environment being used together with an unbatched policy:

    ↗ Policy ↘                 ↗ Policy ↘
... → Policy → ⇒ Environment ⇒ → Policy → ⇒ Environment ⇒ ...
    ↘ Policy ↗                 ↘ Policy ↗

or, even better, a batched environment used together with a batched policy:

... ⇒ Policy ⇒ Environment ⇒ Policy ⇒ Environment ⇒ ...

NOTE: Note that a batched policy is always usable as a policy (the batch conversions are handled automatically), and the same is true for batched environments.