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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.
OpenAI presents LOLA (Learning with Opponent-Learning Awareness), a multi-agent reinforcement learning method where agents shape the anticipated learning of other agents. The approach demonstrates emergence of cooperation in iterated prisoner's dilemma and convergence to Nash equilibrium in game-theoretic settings.