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New R2D-RL environment simplifies multi-agent reinforcement learning for robot soccer

By PulseAugur Editorial · [2 sources] ·

Researchers have developed R2D-RL, a new reinforcement learning environment designed to bridge the gap between the RoboCup 2D Soccer Simulation (RCSS2D) platform and modern Python-based multi-agent reinforcement learning (MARL) workflows. This environment facilitates easier integration with RCSS2D and HELIOS player clients through shared-memory communication and cycle-level synchronization. R2D-RL offers features such as configurable opponents, hybrid action spaces, action masks, reward shaping, and parallel execution, supporting both full-field and scenario-based training. AI

IMPACT Simplifies MARL research for complex robotic systems like robot soccer.

RANK_REASON The cluster contains a research paper detailing a new environment for multi-agent reinforcement learning.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New R2D-RL environment simplifies multi-agent reinforcement learning for robot soccer

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Haobin Qin, Baofeng Zhang, Hidehisa Akiyama, Keisuke Fujii ·

    R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning

    arXiv:2606.18786v1 Announce Type: new Abstract: Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long-horizon tactical behavior. RoboCup 2D Soccer S…

  2. arXiv cs.AI TIER_1 English(EN) · Keisuke Fujii ·

    R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning

    Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long-horizon tactical behavior. RoboCup 2D Soccer Simulation (RCSS2D) provides a mature robot-socce…

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