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This paper proposes SIGMA, a hierarchical collaboration framework for cooperative multi-agent reinforcement learning that learns robust representations under noisy observations by exploiting cooperation structures through density-based grouping and aggregation methods.
OpenAI and University of Oxford researchers present LOLA (Learning with Opponent-Learning Awareness), a reinforcement learning method that enables agents to model and account for the learning of other agents, discovering cooperative strategies in multi-agent games like the iterated prisoner's dilemma and coin game.