XuanPolicy is an open-source ensemble of Deep Reinforcement Learning (DRL) algorithm implementations.
We call it as Xuan-Ce (玄策) in Chinese. "Xuan" means incredible and magic box, "Ce" 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.
- 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]
- 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]
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()
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/
If everything going well, you should get a similar display like below.
To visualize the training scores, training times and the performance, you need to initialize the environment as
env = MonitorVecEnv(DummyVecEnv(...))
then, after training terminated, two extra files "xxx.npy" and "xxx.gif" will be generated in the "./results/" directory. The "xxx.npy" record the scores and clock time for each episode in training. But we haven't provided a plotter.py to draw the curves for this.
@article{XuanPolicy2023,
author = {Wenzhang Liu, Wenzhe Cai, Kun Jiang, and others},
title = {XuanPolicy: A Comprehensive Deep Reinforcement Learning Library},
year = {2023}
}