Belief-Enriched Pessimistic Q-Learning against Adversarial State
  Perturbations
Xiaolin Sun Zizhan Zheng
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, the former does not provide sufficient protection against
strong attacks, while the latter is computationally prohibitive for large
environments. In this work, we propose a new robust RL algorithm for deriving a
pessimistic policy to safeguard against an agent's uncertainty about true
states. This approach is further enhanced with belief state inference and
diffusion-based state purification to reduce uncertainty. Empirical results
show that our approach obtains superb performance under strong attacks and has
a comparable training overhead with regularization-based methods. Our code is
available at https://github.com/SliencerX/Belief-enriched-robust-Q-learning.