/xuance_v0

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Primary LanguagePythonMIT LicenseMIT

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

PyPI Documentation Status GitHub Pepy Total Downlods GitHub Repo stars GitHub forks GitHub watchers

PyTorch TensorFlow MindSpore

Python gym gymnasium pettingzoo

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.

📖 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 training speed with parallel environments.
  • 📈 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 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)

(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]
  • 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]

Currently Supported Environments

Classic Control

(Click to hide)

CartPole
Pendulum
Acrobot
...

Box2D

(Click to hide)

CartPole
Pendulum
Acrobot

MuJoCo Environments

(Click to hide)

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
...

Magent2

(Click to hide)

Battle
Tiger Deer
Battle Field
...

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.

Basic 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()

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

Community

Github Issue

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

Communication Group on QQ App.

Welcome to join the official communication group with QQ app. (Group number: 552432695)

(QR code for QQ group )

@TFBestPractices

Citations

@misc{XuanCe2023,
    title={XuanCe: 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},
}