Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations

arXiv:2403.04050v1 Announce Type: new
Abstract: Reinforcement learning (RL) has achieved phenomenal success in various domains. However, its data-driven nature also introduces new vulnerabilities that can be exploited by malicious opponents. Recent work shows that a well-trained RL agent can be easily manipulated by strategically perturbing its state observations at the test stage. Existing solutions either introduce a regularization term to improve the smoothness of the trained policy against perturbations or alternatively train the agent's policy and the attacker's policy. However, …

adversarial arxiv belief cs.lg q-learning state type