XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

XuanCe 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.

Paper link: https://arxiv.org/pdf/2312.16248.pdf

📖 Full Documentation | 中文文档 📖

Why XuanCe?

Features of XuanCe

• 🎒 Highly modularized.
• 👍 Easy to learn, easy for installation, and easy for usage.
• 🔀 Flexible for model combination.
• 🎉 Abundant algorithms with various tasks.
• 👫 Supports both DRL and MARL tasks.
• 🔑 High compatibility for different users. (PyTorch, TensorFlow2, MindSpore, CPU, GPU, Linux, Windows, MacOS, etc.)
• ⚡ Fast running speed with parallel environments.
• 💻 Distributed training with multi-GPUs.
• 📈 Good visualization effect with tensorboard or wandb tool.

Currently Included Algorithms

👉 DRL

(Click to show supported DRL algorithms)

• 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 - 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)

(Click to show supported MARL algorithms)

• 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]
• Independent Actor-Critic - IAC [Paper] [Code]
• Counterfactual Multi-agent Policy Gradient - COMA [Paper] [Code]
• Value-Decomposition Actor-Critic - VDAC [Paper] [Code]
• Independent Proximal Policy Optimization - IPPO [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]

Currently Supported Environments

Classic Control

(Click to hide)

Cart Pole

Pendulum

Acrobot

...

Box2D

(Click to hide)

Bipedal Walker

Car Racing

Lunar Lander

MuJoCo Environments

(Click to hide)

Ant

HalfCheetah

Hopper

Humanoid

...

Atari Environments

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Breakout

Boxing

Alien

Adventure

Air Raid

...

Minigrid Environments

(Click to hide)

Crossing

Memory

Locked Room

Playground

...

Drones Environments

XuanCe's documentation for the installation and usage of gym-pybullet-drones.

(Click to hide)

Helix

Single-Agent Hover

Multi-Agent Hover

...

MPE Environments

(Click to hide)

Simple Push

Simple Reference

Simple Spread

...

SMAC

Google Research Football

👉 Installation

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

Before installing XuanCe, 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 XuanCe by the following steps.

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

conda create -n xuance_env python=3.7

Step 2: Activate conda environment:

conda activate xuance_env

Step 3: Install the library:

pip install xuance

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

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

👉 Quickly Start

Train a Model

import xuance

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

Test the Model

import xuance

runner_test = xuance.get_runner(method='dqn',
                                env='classic_control',
                                env_id='CartPole-v1',
                                is_test=True)
runner_test.run()

Visualize the results

Tensorboard

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

Weights & Biases (wandb)

XuanCe also supports Weights & Biases (wandb) tools for users to visualize the results of the running implementation.

How to use wandb online? ➡️ https://github.com/wandb/wandb.git/

How to use wandb offline? ➡️ https://github.com/wandb/server.git/

Community

Github Issue

You can put your questions, advices, or the bugs you have found in the Issues.

Social Accounts.

Welcome to join the official communication group with QQ app (Group number: 552432695), and the official account ("玄策 RLlib") on WeChat.

(QR code for QQ group and WeChat official account)

QQ group

Official account (WeChat)

@TFBestPractices

Citations

@article{liu2023xuance,
  title={XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library},
  author={Liu, Wenzhang and Cai, Wenzhe and Jiang, Kun and Cheng, Guangran and Wang, Yuanda and Wang, Jiawei and Cao, Jingyu and Xu, Lele and Mu, Chaoxu and Sun, Changyin},
  journal={arXiv preprint arXiv:2312.16248},
  year={2023}
}