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Wen-Zhang Liu. School of AI, Anhui University. I am focusing on deep reinforcement learning and multi-agent reinforcment learning. Hefei, China. Email. DBLP. Github. Google Scholar. Zhihu. Brief Bio (Wen-Zhang Liu, 柳文章) Presently, I serve as a research scientist at the School of Artificial Intelligence, Anhui University.
7. 2018. Knowledge transfer in multi-agent reinforcement learning with incremental number of agents. W Liu, L Dong, J Liu, C Sun. Journal of systems engineering and electronics 33 (2), 447-460. , 2022. 4. 2022. Credit assignment in heterogeneous multi-agent reinforcement learning for fully cooperative tasks.
Wen-Zhang Liu wenzhangliu. Follow. I am interested in machine learning, deep reinforcement learning, multi-agent reinforcement learning, and transfer learning. 23 followers · 12 following. School of AI, AHU. Hefei, China. 12:18 (UTC +08:00) https://wenzhangliu.xyz. Achievements. Beta Send feedback. Block or Report. Pinned. agi-brain/xuance Public.
- Hefei, China
- School of AI, AHU
- XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
- Why XuanCe?
- Currently Included Algorithms
- Currently Supported Environments
- 👉 Installation
- 👉 Quickly Start
- Community
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 📖
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. •📈 Good visualization effect with tensorboard or wandb tool.
👉 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]
(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]
Classic Control(Click to hide)Box2D(Click to hide)MuJoCo Environments(Click to hide)💻 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):
Step 2: Activate conda environment:
Step 3: Install the library:
Train a ModelTest the ModelVisualize the resultsGithub 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
Dec 25, 2023 · Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun. In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore.
Wenzhang Liu, Lu Dong, Dan Niu, Changyin Sun: Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features. IEEE CAA J. Autom. Sinica 9 (9): 1673-1686 (2022)
Multi-agent reinforcement learning algorithms: MADDPG-SFs & MADDPG-SFKT. This is an open-source code for our research work titled "Efficient Exploration for Multi-agent Reinforcement Learning via Transferable Successor Features".
