Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
Robustness under latent distribution shift remains challenging in partially observable reinforcement learning. We formalize a focused setting where an adversary selects a hidden initial latent distribution before the episode, termed an adversarial latent-initial-state POMDP. Theoretically, we prove a latent minimax principle, characterize worst-case defender distributions, and derive approximate best-response inequalities with finite-sample concentration bounds that make the optimization and sampling terms explicit. Empirically, using a Battleship benchmark, we demonstrate that targeted exposure to shifted latent distributions reduces average robustness gaps between Spread and Uniform dis...
| Authors | Angad Singh Ahuja |
|---|---|
| Primary category | cs.LG |
| Published | 7 March 2026 |
| Last updated at source | 8 August 2026 |
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