Modular codebase for reinforcement learning models training, testing and visualization.

Contributors: Bryan M. Li, Alexander Cowen-Rivers, Piotr Kozakowski, David Tao, Siddhartha Rao Kamalakara, Nitarshan Rajkumar, Hariharan Sezhiyan, Sicong Huang, Aidan N. Gomez

• Agents: DQN, Vanilla Policy Gradient, DDPG, PPO
• Environments:
  • OpenAI Gym
    • support both Discrete and Box environments
    • render (--render) and save (--record_video) environment replay
  • OpenAI Atari
  • OpenAI ProcGen
• Model-free asynchronous training (--num_workers)
• Memory replay: Simple, Proportional Prioritized Experience Replay
• Modularized
  • hyper-parameters setting (--hparams)
  • action functions)
  • compute gradient functions
  • advantage estimation
  • learning rate schemes

Example for recorded envrionment on various RL agents.

MountainCar-v0	Pendulum-v0	VideoPinball-v0	procgen-coinrun-v0

It is recommended to install the codebase in a virtual environment (virtualenv or conda).

Configure use_gpu and (if on OSX) mac_package_manager (either macports or homebrew) params in setup.sh, then run it as

sh setup.sh

You need to install the following for your system:

• TensorFlow
• OpenAI Gym
• OpenAI Atari
• OpenAI ProcGen
• FFmpeg
• Additional python packages pip install -r ../requirements.txt

# start training
python train.py --sys ... --hparams ... --output_dir ...
# run tensorboard
tensorboard --logdir ...
# test agnet
python train.py --sys ... --hparams ... --output_dir ... --test_only --render

Check available flags with --help, defaults.py for default hyper-parameters, and check hparams/dqn.py agent specific hyper-parameters examples.

• hparams: Which hparams to use, defined under rl/hparams
• sys: Which system environment to use.
• env: Which RL environment to use.
• output_dir: The directory for model checkpoints and TensorBoard summary.
• train_steps:, Number of steps to train the agent.
• test_episodes: Number of episodes to test the agent.
• eval_episodes: Number of episodes to evaluate the agent.
• test_only: Test agent without training.
• copies: Number of independent training/testing runs to do.
• render: Render game play.
• record_video: Record game play.
• num_workers, number of workers.

More detailed documentation can be found here.

We'd love to accept your contributions to this project. Please feel free to open an issue, or submit a pull request as necessary. Contact us team@for.ai for potential collaborations and joining FOR.ai.