Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents

Understand

Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern.

• Numerous studies have investigated the mechanisms of attacks on the RL agent's state space.
• Nonetheless, attacks on the RL agent's action space (AS) (corresponding to actuators in engineering systems) are equally perverse; such attacks are relatively less studied in the ML literature.
• In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack.