LEARNING
R2D-RL：面向多智能体强化学习的RoboCup 2D足球环境
Haobin Qin, Baofeng Zhang, Hidehisa Akiyama, Keisuke Fujii
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出了R2D-RL，一个将RoboCup 2D足球仿真与Python多智能体强化学习接口连接的环境，支持全场和基于场景的训练。通过共享内存通信和周期级同步，该环境提供了可配置对手、离散和混合动作空间、EPV奖励塑形及并行执行功能。
关键词
RoboCupmulti-agent reinforcement learningsoccer simulationshared-memory communicationreward shaping
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