• XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library
• • Currently Included Algorithms
  • • DRL
    • Multi-Agent Reinforcement Learning (MARL)
  • Supported Environments
  • • Toy Environments (Classic Control, Box2D, etc.)
    • MuJoCo Environments
    • Atari Environments
    • MPE Environments
    • Magent
  • Installation
  • Basic Usage
  • • Quickly Start
    • • Train a Model
      • Test the Model
  • Logger
  • Selected Results
  • • Toy Environments
    • Mujoco Environments
    • MPE Environments

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 | GitHub | XuanCe (Mini version) |

Currently Included Algorithms

DRL

(Click to hide)

• 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 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]
• DQN with Convolutional Neural Network - C-DQN [Paper]
• DQN with Long Short-term Memory - L-DQN [Paper]
• DQN with CNN and Long Short-term Memory - CL-DQN [Paper]
• DQN with Quantile Regression - QRDQN [Paper]
• Distributional Reinforcement Learning - C51 [Paper]
• 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)

(Click to hide)

• 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.)

(Click to hide)

CartPole

Pendulum

Lunar_lander

...

MuJoCo Environments

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Ant

HalfCheetah

Hopper

Humanoid

...

Atari Environments

(Click to hide)

Breakout

Boxing

Alien

Adventure

Air Raid

...

MPE Environments

(Click to hide)

Simple Push

Simple Reference

Simple Spread

...

Magent

(Click to hide)

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.

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: Download the library via git:

git clone https://openi.pcl.ac.cn/OpenRelearnware/XuanPolicy.git

Step 4: Change directory to the directory of XuanPolicy:

cd XuanPolicy

Step 5: Install the library:

pip install -e .

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(method='dqn', env='Classic_Control', env_id='CartPole-v1', is_test=False)
runner.run()

Test the Model

import xuanpolicy as xp

runner_test = xp.get_runner(method='dqn', env='Classic_Control', env_id='CartPole-v1', 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-v1

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, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun},
    publisher = {GitHub},
    year={2023},
}