Generalized Intention Modeling in Multi-Agent Reinforcement Learning

ArXi:2605.31318v1 Announce Type: new Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent's next action or a future environment state, and use this to guide the ego-agent's behavior.