Used in the paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. To install, cd into the root directory and type pip install -e . make_env.py: contains code for …

MPE （multiagent particle environment）是由OpenAI开发的一套时间离散、空间连续的二维多智能体环境，该环境通过控制二维空间中不同角色粒子（particle）的运动来完成一系列任务， …

在多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）的研究和应用中，构建合适的环境来测试和评估算法是非常重要的。 以下是一些常用的多智能体强化学习环境，它们涵盖 …

Concise pytorch implements of MARL algorithms, including MAPPO, MADDPG, MATD3, QMIX and VDN.

Multi-particle Environments (MPE) are a set of communication-oriented environments where particle agents can (sometimes) move, communicate, see each other, push each other …

MARLlib unifies diverse algorithm pipelines with agent-level distributed dataflow, allowing researchers to develop, test, and evaluate MARL algorithms across different tasks and …

JaxMARL combines ease-of-use with GPU-enabled efficiency, and supports a wide range of commonly used MARL environments as well as popular baseline algorithms. Our aim is for …

在完全观测情况下，独立学习算法跟 MARL 算法性能近似，两者的差异往往在部分观测和需要密切合作的 SMAC 等任务中体现。 整体来讲， 独立学习算法中 IPPO>IA2C>IQL，CTDE 算法中 …

Feb 28, 2021 · MPE 环境是一个时间离散、空间连续的二维环境，UI的界面风格如图所示，它通过控制在二维空间中代表不同含义的粒子，实现对于各类MARL 算法 的验证。 MPE被人们广泛 …

MARBLER offers a robust and comprehensive evaluation platform for MRRL by marrying Georgia Tech’s Robotarium (which enables rapid deployment on physical MRS) and OpenAI’s Gym …