agi-brain / xuanpolicy

XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library

XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library

XuanPolicy is an open-source ensemble of Deep Reinforcement Learning (DRL) algorithm implementations.

We call it as Xuan-Ce (玄策) in Chinese. "Xuan (玄)" means incredible and magic box, "Ce (策)" means policy.

DRL algorithms are sensitive to hyper-parameters tuning, varying in performance with different tricks, and suffering from unstable training processes, therefore, sometimes DRL algorithms seems elusive and "Xuan". This project gives a thorough, high-quality and easy-to-understand implementation of DRL algorithms, and hope this implementation can give a hint on the magics of reinforcement learning.

We expect it to be compatible with multiple deep learning toolboxes( PyTorch, TensorFlow, and MindSpore), and hope it can really become a zoo full of DRL algorithms.

| Full Documentation | 中文文档 | OpenI (启智社区) | XuanCe (Mini version) |

Currently Included Algorithms

DRL

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• Deep Q Network - DQN [Paper]
• DQN with Double Q-learning - Double DQN [Paper]
• DQN with Dueling network - Dueling DQN [Paper]
• DQN with Prioritized Experience Replay - PER [Paper]
• DQN with Parameter Space Noise for Exploration - NoisyNet [Paper]
• Deep Recurrent Q-Netwrk - DRQN [Paper]
• DQN with Quantile Regression - QRDQN [Paper]
• Distributional Reinforcement Learning - C51 [Paper]
• Vanilla Policy Gradient - PG [Paper]
• Phasic Policy Gradient - PPG [Paper] [Code]
• Advantage Actor Critic - A2C [Paper] [Code]
• Soft actor-critic based on maximum entropy - SAC [Paper] [Code]
• Soft actor-critic for discrete actions - SAC-Discrete [Paper] [Code]
• Proximal Policy Optimization with clipped objective - PPO-Clip [Paper] [Code]
• Proximal Policy Optimization with KL divergence - PPO-KL [Paper] [Code]
• Deep Deterministic Policy Gradient - DDPG [Paper] [Code]
• Twin Delayed Deep Deterministic Policy Gradient - TD3 [Paper][Code]
• Parameterised deep Q network - P-DQN [Paper]
• Multi-pass parameterised deep Q network - MP-DQN [Paper] [Code]
• Split parameterised deep Q network - SP-DQN [Paper]

Multi-Agent Reinforcement Learning (MARL)

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• Independent Q-learning - IQL [Paper] [Code]
• Value Decomposition Networks - VDN [Paper] [Code]
• Q-mixing networks - QMIX [Paper] [Code]
• Weighted Q-mixing networks - WQMIX [Paper] [Code]
• Q-transformation - QTRAN [Paper] [Code]
• Deep Coordination Graphs - DCG [Paper] [Code]
• Independent Deep Deterministic Policy Gradient - IDDPG [Paper]
• Multi-agent Deep Deterministic Policy Gradient - MADDPG [Paper] [Code]
• Counterfactual Multi-agent Policy Gradient - COMA [Paper] [Code]
• Multi-agent Proximal Policy Optimization - MAPPO [Paper] [Code]
• Mean-Field Q-learning - MFQ [Paper] [Code]
• Mean-Field Actor-Critic - MFAC [Paper] [Code]
• Independent Soft Actor-Critic - ISAC
• Multi-agent Soft Actor-Critic - MASAC [Paper]
• Multi-agent Twin Delayed Deep Deterministic Policy Gradient - MATD3 [Paper]

Supported Environments

Toy Environments (Classic Control, Box2D, etc.)

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CartPole

Pendulum

Lunar_lander

...

MuJoCo Environments

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Ant

HalfCheetah

Hopper

Humanoid

...

Atari Environments

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Breakout

Boxing

Alien

Adventure

Air Raid

...

MPE Environments

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Simple Push

Simple Reference

Simple Spread

...

Magent

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Battle

Tiger Deer

Battle Field

...

Installation

The library can be run at Linux, Windows, MacOS, and EulerOS, etc.

Before installing XuanPolicy, you should install Anaconda to prepare a python environment. (Note: select a proper version of Anaconda from here.)

After that, open a terminal and install XuanPolicy by the following steps.

Step 1: Create a new conda environment (python>=3.7 is suggested):

conda create -n xpolicy python=3.7

Step 2: Activate conda environment:

conda activate xpolicy

Step 3: Install the library:

pip install xuanpolicy

This command does not include the dependencies of deep learning toolboxes. To install the XuanPolicy with deep learning tools, you can type pip install xuanpolicy[torch] for PyTorch, pip install xuanpolicy[tensorflow] for TensorFlow2, pip install xuanpolicy[mindspore] for MindSpore, and pip install xuanpolicy[all] for all dependencies.

Note: Some extra packages should be installed manually for further usage.

Basic Usage

Quickly Start

Train a Model

import xuanpolicy as xp

runner = xp.get_runner(agent_name='dqn', env_name='classic_control/CartPole-v0', is_test=False)
runner.run()

Test the Model

import xuanpolicy as xp

runner_test = xp.get_runner(agent_name='dqn', env_name='classic_control/CartPole-v0', is_test=True)
runner_test.run()

Logger

You can use tensorboard to visualize what happened in the training process. After training, the log file will be automatically generated in the directory ".results/" and you should be able to see some training data after running the command.

$ tensorboard --logdir ./logs/dqn/torch/CartPole-v0

Selected Results

Toy Environments

Mujoco Environments

MPE Environments

@misc{XuanPolicy2023,
    title={XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library},
    author={Wenzhang Liu, Wenzhe Cai, Kun Jiang, Yuanda Wang, Guangran Cheng, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun},
    publisher = {GitHub},
    year={2023},
}