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 (torch, tensorflow, and mindspore), and hope it can really become a zoo full of DRL algorithms.
This project is supported by Peng Cheng Laboratory.
(Click to show all DRL agents)
- 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]
(Click to show all MARL agents)
- 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]
Toy Environments (Classic Control, Box2D, PlatformDomain, etc.)
(Click to show Toy environments)
 CartPole |
 Pendulum |
 Acrobot |
 MountainCar |
 Lunar_lander |
 PlatformDomain |
... |
(Click to show MuJoCo environments)
 Ant |
 HalfCheetah |
 Hopper |
 Humanoid |
 InvertedPendulum |
 Reacher |
 Swimmer |
 Walker2d |
(Click to show Atari environments)
(Click to show MPE environments)
(Click to show Magent environments)
The library can be run at Linux, Windows, MacOS, and Euler OS, etc.
Before installing XuanPolicy, you should install Anaconda to prepare a python environment.
After that, create a terminal and install XuanPolicy by the following steps.
Step 1: Create and activate a new conda environment (python>=3.7 is suggested):
conda create -n xuanpolicy python=3.7
conda activate xuanpolicy
Step 2: 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 TensorFlow, 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.
import xuanpolicy as xp
runner = xp.get_runner(agent_name='dqn', env_name='toy_env/CartPole-v0', is_test=False)
runner.run()import xuanpolicy as xp
runner_test = xp.get_runner(agent_name='dqn', env_name='toy_env/CartPole-v0', is_test=True)
runner_test.run()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
If everything going well, you should get a similar display like below.
@article{XuanPolicy2023,
author = {Wenzhang Liu, Wenzhe Cai, Kun Jiang, and others},
title = {XuanPolicy: A Comprehensive Deep Reinforcement Learning Library},
year = {2023}
}