Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network
This addresses robustness issues in deep reinforcement learning for applications like autonomous systems, though it is incremental as it builds on existing adversarial attack techniques.
The paper tackles the problem of improving policy robustness in deep reinforcement learning agents by proposing a gradient-based adversarial attack on the critic network to generate environment disturbances, resulting in significantly better robustness improvements compared to existing methods.
To improve policy robustness of deep reinforcement learning agents, a line of recent works focus on producing disturbances of the environment. Existing approaches of the literature to generate meaningful disturbances of the environment are adversarial reinforcement learning methods. These methods set the problem as a two-player game between the protagonist agent, which learns to perform a task in an environment, and the adversary agent, which learns to disturb the protagonist via modifications of the considered environment. Both protagonist and adversary are trained with deep reinforcement learning algorithms. Alternatively, we propose in this paper to build on gradient-based adversarial attacks, usually used for classification tasks for instance, that we apply on the critic network of the protagonist to identify efficient disturbances of the environment. Rather than learning an attacker policy, which usually reveals as very complex and unstable, we leverage the knowledge of the critic network of the protagonist, to dynamically complexify the task at each step of the learning process. We show that our method, while being faster and lighter, leads to significantly better improvements in policy robustness than existing methods of the literature.