Reviews: Policy Poisoning in Batch Reinforcement Learning and Control

The paper studies the problem of policy poisoning in batch reinforcement learning and control where the learner estimates the model of the world from batch data set, and finds an optimal policy with respect to the learned model. The attacker modifies the data by the means of modifying the reward entries to make the agent learn a target policy. The paper presents a framework for solving batch policy poison attacks on two standard victims. The theoretical and experimental results show some evidence for the feasibility of policy poisoning attacks. Overall, I think this is an interesting paper that is motivated under a realistic adversarial setting where the attacker can alter the reward (instead of altering the dynamics of the world) to change the optimal policy to an adversarial target policy. The paper is easy to read due to its clear organization.