RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
This work addresses the need for quantitative benchmarking of adversarial robustness in DRL policies, which is crucial for deploying reliable AI systems in safety-critical domains, though it appears incremental as it builds on existing methods for specific vulnerabilities.
The paper tackled the problem of assessing adversarial resilience and robustness in deep reinforcement learning policies by disentangling vulnerabilities from representation learning and state transition shifts, and proposed two RL-based benchmarking techniques, demonstrating feasibility with DQN, A2C, and PPO2 policies in the Cartpole environment.
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DRL policies to distributional shifts in state transitions. Building on this approach, we propose two RL-based techniques for quantitative benchmarking of adversarial resilience and robustness in DRL policies against perturbations of state transitions. We demonstrate the feasibility of our proposals through experimental evaluation of resilience and robustness in DQN, A2C, and PPO2 policies trained in the Cartpole environment.