Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error

Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness and suggest the potential lack of an optimal robust policy (ORP), posing challenges in setting strict robustness constraints. This work further investigates ORP: At first, we introduce a consistency assumption of policy (CAP) stating that optimal actions in the Markov decision process remain consistent with minor perturbations, supported by empirical and theoretical evidence. Building upon CAP, we crucially prove …

adversarial agents attacks challenges constraints cs.lg error explore policy q-learning reinforcement reinforcement learning robust robustness state studies work