Reinforcement Learning Toolbox for Robotics

Toolbox with highly optimized implementations of deep reinforcement learning algorithms for robotics using Pytorch and Python.

In this project, two colleagues and I develop a toolbox with state of the art reinforcement learning algorithms using Pytorch and Python. The toolbox contains other useful features such as custom robotics environments, loggers, plotters and much more.

About The Project

Toolbox Structure

All the algorithms are in the rlbotics directory. Each algorithm specified above has an individual directory.

List of Algorithms

1. Deep Q Network (DQN)
2. Double Deep Q Network (DDQN)
3. Deep Deterministic Policy Gradient (DDPG)
4. Twin Delayed Deep Deterministic Policy Gradient (TD3)
5. Vanilla Policy Gradient (VPG)
6. Soft Actor Critic (SAC)
7. Trust Region Policy Optimization (TRPO)
8. Proximal Policy Optimization (PPO)

Common

The directory common contains common modular classes to easily build new algorithms.

• approximators: Basic Deep Neural Networks (Dense, Conv, LSTM).
• logger: Log training data and other information
• visualize: Plot graphs
• policies: Common policies such as Random, Softmax, Parametrized Softmax and Gaussian Policy
• utils: Functions to compute the expected return, the Generalized Advantage Estimation (GAE), etc.

Algorithm Directories

Each algorithm directory contains at least 3 files:

• main.py: Main script to run the algorithm
• hyperparameters.py: File to contain the default hyperparameters
• <algo>.py: Implementation of the algorithm
• utils.py: (Optional) File containing some utility functions

Some algorithm directories may have additional files specific to the algorithm.

Contributing

To contribute to this package, it is recommended to follow this structure:

• The new algorithm directory should at least contain the 3 files mentioned above.

• main.py should contain at least the following functions:

  • argparse: Parses input argument and loads default hyperparameters from hyperparameter.py.
  • main: Parses input argument, builds the environment and agent, and train the agent.
  • train: Main training loop called by main()

• <algo>.py should contain at least the following methods:

  • __init__: Initializes the classes
  • _build_policy: Build policy
  • _build_value_function: Build value function
  • compute_policy_loss: Build policy loss function
  • update_policy: Update the policy
  • update_value: Update the value function

Getting Started

Prerequisites

• The program was created using Python3.7
• Pytorch
• Numpy
• Pandas
• Tensorboard
• Seaborn
• Scipy
• Gym

Installation

To install the RLBotics toolbox, install the required librarires and clone this repository using the following commands:

pip install -r requirements.txt
git clone https://github.com/dyumanaditya/rlbotics

Usage

To run the an algorithm on a particular environment, open a terminal and navigate to the folder you just cloned and run the following command:

python3 -m rlbotics.algo.main

Where algo can be replaced by the algorithm you wish to use. You can also pass in arguments, or modify the hyperparameters.py file contained in each algorithm folder to change the environment and other hyperparameters related to the algorithm.

Once the algorithm is running you can deploy a tensorboard session to track the progress.

License

Distributed under the BSD-3-Clause License. See LICENSE for more information.

Contact

Dyuman Aditya - [email protected]

Kousheek Chakraborty - [email protected]

Suman Pal - [email protected]