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

[Submitted on 17 Jun 2026 (v1), last revised 25 Jun 2026 (this version, v2)]

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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.

Submission history

From: Haobin Qin [view email]
[v1] Wed, 17 Jun 2026 07:57:06 UTC (6,181 KB)
[v2] Thu, 25 Jun 2026 05:55:34 UTC (1,713 KB)