Yue Gao

Buqing Nie, Yangqing Fu, Jingtian Ji et al.

Reinforcement Learning (RL) has achieved remarkable success in sequential decision tasks. However, recent studies have revealed the vulnerability of RL policies to different perturbations, raising concerns about their effectiveness and safety in real-world applications. In this work, we focus on the robustness of RL policies against action perturbations and introduce a novel framework called Optimal Adversary-aware Policy Iteration (OA-PI). Our framework enhances action robustness under various perturbations by evaluating and improving policy performance against the corresponding optimal adversaries. Besides, our approach can be integrated into mainstream DRL algorithms such as Twin Delayed DDPG (TD3) and Proximal Policy Optimization (PPO), improving action robustness effectively while maintaining nominal performance and sample efficiency. Experimental results across various environments demonstrate that our method enhances robustness of DRL policies against different action adversaries effectively.

Jianmina Ma, Jingtian Ji, Yue Gao

Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. However, constrained reinforcement learning methods face challenges in striking the right balance between task performance and constraint satisfaction and it is prone for them to get stuck in over-conservative or constraint violating local minima. In this paper, we propose Adversarial Constrained Policy Optimization (ACPO), which enables simultaneous optimization of reward and the adaptation of cost budgets during training. Our approach divides original constrained problem into two adversarial stages that are solved alternately, and the policy update performance of our algorithm can be theoretically guaranteed. We validate our method through experiments conducted on Safety Gymnasium and quadruped locomotion tasks. Results demonstrate that our algorithm achieves better performances compared to commonly used baselines.