Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic

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Other Links: arxiv.org

Abstract:

Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior. While MA-AIRL has promising results on cooperative and competitive tasks, it is sample-...More

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