pytorch-madrl

This project includes PyTorch implementations of various Deep Reinforcement Learning algorithms for both single agent and multi-agent.

• A2C
• ACKTR
• DQN
• DDPG
• PPO

It is written in a modular way to allow for sharing code between different algorithms. In specific, each algorithm is represented as a learning agent with a unified interface including the following components:

• interact: interact with the environment to collect experience. Taking one step forward and n steps forward are both supported (see _take_one_step_ and _take_n_steps, respectively)
• train: train on a sample batch
• exploration_action: choose an action based on state with random noise added for exploration in training
• action: choose an action based on state for execution
• value: evaluate value for a state-action pair
• evaluation: evaluation the learned agent

Requirements

• gym
• python 3.6
• pytorch

Usage

To train a model:

$ python run_a2c.py

Results

It's extremely difficult to reproduce results for Reinforcement Learning algorithms. Due to different settings, e.g., random seed and hyper parameters etc, you might get different results compared with the followings.

A2C

ACKTR

DDPG

DQN

PPO

TODO

• TRPO
• LOLA
• Parameter noise

Acknowledgments

This project gets inspirations from the following projects:

• Ilya Kostrikov's pytorch-a2c-ppo-acktr (kfac optimizer is taken from here)
• OpenAI's baselines

License

MIT