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
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- 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)
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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
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CartPole
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Pendulum
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Lunar_lander
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...
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Ant
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HalfCheetah
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Hopper
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Humanoid
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...
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Breakout
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Boxing
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Alien
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Adventure
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Air Raid
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...
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Simple Push
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Simple Reference
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Simple Spread
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...
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Battle
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Tiger Deer
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Battle Field
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...
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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},
}