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

In three linesR2D-RL bridges RoboCup 2D Soccer Simulator (RCSS2D) to Python MARL workflows via shared-memory communication. The environment supports full-field and scenario-based training with discrete/hybrid action spaces, action masks, EPV-based reward shaping, and parallel execution. Includes 11-vs-11 full-field benchmarks and baseline results.

Summary generated by Claude — human-verified