# Peer Adaptation

Opponent modeling in multi-agent reinforcement learning usually concatenates an inferred peer representation with the current observation and encodes them jointly, leaving their relationship for policy learning to discover implicitly. PCOR instead imposes an asymmetric structure: observation features form the base representation of the current interaction state, and the peer representation only modulates how those features are interpreted. On two partially observable benchmarks, PCOR reaches higher final evaluation performance than the reported baselines on unseen peers; on POO, against a concatenation variant sharing the same encoders and differing only in fusion, evaluation success rises from 30.2% to 51.1%.

Opponent modeling in multi-agent reinforcement learning usually concatenates an inferred peer representation with the current observation and encodes them jointly, leaving their relationship for policy learning to discover implicitly. PCOR instead imposes an asymmetric structure: observation features form the base representation of the current interaction state, and the peer representation only modulates how those features are interpreted. On two partially observable benchmarks, PCOR reaches higher final evaluation performance than the reported baselines on unseen peers; on POO, against a concatenation variant sharing the same encoders and differing only in fusion, evaluation success rises from 30.2% to 51.1%.