各种DRL算法的PyTorch实现
该项目包括用于单个代理和多代理的各种Deep Reinforcement Learning算法的PyTorch实现。
Python 机器学习
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详细介绍
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