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.

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.