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OpenAI researchers derive a bias-free action-dependent baseline for variance reduction in policy gradient methods, demonstrating improved learning efficiency on high-dimensional control tasks, multi-agent, and partially observed environments.
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.
OpenAI introduces Proximal Policy Optimization (PPO), a reinforcement learning algorithm that matches or outperforms state-of-the-art methods while being simpler to implement and tune. PPO uses a novel clipped objective function to constrain policy updates and has since become OpenAI's default RL algorithm.