AI RESEARCH

Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees

arXiv CS.AI

ArXi:2604.14243v1 Announce Type: cross Real-world decision-making systems operate in environments where state transitions depend not only on the agent's actions, but also on \textbf{exogenous factors outside its control}--competing agents, environmental disturbances, or strategic adversaries--formally, $s_{h+1} = f(s_h, a_h, \bar{a}_h)+\omega_h$ where $\bar{a}_h$ is the adversary/external action, $a_h$ is the agent's action, and $\omega_h$ is an additive noise.