ChenglongChen / Pytorch Drl
Licence: mit
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
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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
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