1 citations · 2 across the 4 of their papers we have counts for
4 papers
Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error
Haoran Li, Zicheng Zhang, Wang Luo +4
Ensuring the robustness of deep reinforcement learning (DRL) agents against adversarial attacks is critical for their trustworthy deployment. Recent research highlights the challen…
Dual Alignment Maximin Optimization for Offline Model-based RL
Chi Zhou, Wang Luo, Haoran Li +3
Offline reinforcement learning agents face significant deployment challenges due to the synthetic-to-real distribution mismatch. While most prior research has focused on improving…
Mitigating Distribution Shift in Model-based Offline RL via Shifts-aware Reward Learning
Wang Luo, Haoran Li, Zicheng Zhang +4
Model-based offline reinforcement learning trains policies using pre-collected datasets and learned environment models, eliminating the need for direct real-world environment inter…
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo +4
Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness…