Roman Belaire

On Minimizing Adversarial Counterfactual Error in Adversarial Reinforcement Learning

Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge inherent to adversarial perturbations is that by altering the information observed by the agent, the state becomes only pa…