R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning
Quick Answer
R2D-RL is a new reinforcement learning environment that bridges RoboCup 2D Soccer Simulation with Python-based MARL workflows, enabling advanced multi-agent training.
Quick Take
It features configurable opponents, hybrid action spaces, and supports parallel execution, providing benchmarks for 11-vs-11 scenarios and front-goal challenges.
Key Points
- R2D-RL connects RCSS2D and HELIOS clients via shared-memory communication.
- Supports full-field and scenario-based training with configurable opponents.
- Includes hybrid parameterized action spaces and action masks for enhanced control.
- Offers expected possession value (EPV)-based reward shaping for improved learning.
- Provides benchmarks for 11-vs-11 matches and front-goal scenarios.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 |
Submission history
From: Haobin Qin [view email]
[v1]
Wed, 17 Jun 2026 07:57:06 UTC (6,181 KB)
— Originally published at arxiv.org
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