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

[2606.18786] R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning

[Submitted on 17 Jun 2026]

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

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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 Simulation (RCSS2D) provides a mature robot-soccer platform, but its competition-oriented server-client architecture is difficult to use directly with modern Python-based MARL workflows. We introduce R2D-RL, a reinforcement learning environment that connects RCSS2D and HELIOS-based player clients to a Python MARL interface through shared-memory communication and cycle-level synchronization. R2D-RL supports full-field and scenario-based training with configurable opponents, Base discrete and Hybrid parameterized action spaces, action masks, expected possession value (EPV)-based reward shaping, and parallel execution. We provide front-goal scenarios and an 11-vs-11 full-field benchmark, together with baseline results.

Comments: Code is available at: this https URL

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.18786 [cs.AI]

(or arXiv:2606.18786v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.18786

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haobin Qin [view email] [v1] Wed, 17 Jun 2026 07:57:06 UTC (6,181 KB)

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