Paper introduces adversarial training for robust RL policies

This paper, titled "Adversarial Latent-State Training for Robust Policies in Partially Observable Domains," introduces a new framework for reinforcement learning in partially observable environments. The authors propose an adversarial approach where an adversary sets the initial latent distribution, and they prove a latent minimax principle to characterize worst-case scenarios. Empirically, their method, tested on a Battleship benchmark, significantly reduced robustness gaps between different distribution strategies, showing improved performance with targeted exposure to shifted latent states. AI

IMPACT Introduces a new theoretical framework and empirical validation for improving robustness in partially observable reinforcement learning environments.

RANK_REASON The cluster contains an academic paper with a novel methodology and empirical results. [lever_c_demoted from research: ic=1 ai=1.0]

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• Angad Ahuja
• Battleship
• Proximal Policy Optimization
• Spread
• Uniform

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