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This paper presents a cross-sectional study comparing various action factorization methods (independent networks, shared encoder, VDN, QPLEX, Joint, Auto-Regressive) across three RL algorithm families (PPO, SAC, DQN) in hybrid discrete-continuous action spaces, introducing two new lightweight environments and variants VDN-PPO and PPO-MIX.
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.