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
- gym
- python 3.6
- pytorch
To train a model:
$ python run_a2c.py
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.
- TRPO
- LOLA
- Parameter noise
This project gets inspirations from the following projects:
- Ilya Kostrikov's pytorch-a2c-ppo-acktr (kfac optimizer is taken from here)
- OpenAI's baselines
MIT