Contribute to 15534081591/QMIX development by creating an account on GitHub.

2010年2月4日 · PyMARL is WhiRL 's framework for deep multi-agent reinforcement learning and includes implementations of the following algorithms: QMIX: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning COMA: Counterfactual Multi-Agent Policy Gradients VDN: Value-Decomposition Networks For Cooperative Multi-Agent Learning …

QMIX是一种Value-Based的多智能体强化学习算法，使用中心式学习分布式执行的方法。 算法大框架为基于AC框架的CTDE（Centralized Training Distributed Execution）模式，整个网络由Mixing Network和Agent Network两部分组成。

Code for Weighted QMIX. Contribute to oxwhirl/wqmix development by creating an account on GitHub.

Critic network class for Qmix. Outputs centralized value function predictions given independent q value.

Afterwards, we re-evaluate numerous QMIX variants with normalized the tricks (a general set of hyperparameters), and find that QMIX achieves the SOTA.

在ma_gym环境复现强化学的COMA Qmix和自己改进的代码（In the MA_GYM environment to reproduce the enhanced COMA Qmix and their own improved code） 网络均使用pytorch搭建。 python版本3.6。 (Pytorch is used for all networks.

MARLToolkit is a Multi-Agent Reinforcement Learning Toolkit based on Pytorch. It provides MARL research community a unified platform for developing and evaluating the new ideas in various multi-agent environments. There are four core features of MARLToolkit. it collects most of the existing MARL algorithms widely acknowledged by the community and unifies them under one …

Pytorch implementations of the multi-agent reinforcement learning algorithms, including IQL, QMIX, VDN, COMA, QTRAN (both QTRAN-base and QTRAN-alt), MAVEN, CommNet, DyMA-CL, and G2ANet, which are the state of the art MARL algorithms. In addition, because CommNet and G2ANet need an external training algorithm, we provide Central-V and REINFORCE for …

Concise pytorch implements of MARL algorithms, including MAPPO, MADDPG, MATD3, QMIX and VDN. - Lizhi-sjtu/MARL-code-pytorch