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Offline Safe Policy Optimization From Heterogeneous Feedback
Ze Gong, Pradeep Varakantham, Akshat Kumar
Offline Preference-based Reinforcement Learning (PbRL) learns rewards and policies aligned with human preferences without the need for extensive reward engineering and direct inter…
TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels
Siow Meng Low, Ze Gong, Akshat Kumar
Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured. In many settings, supervision is limited t…
Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning
Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar
In many RL applications, ensuring an agent's actions adhere to constraints is crucial for safety. Most previous methods in Action-Constrained Reinforcement Learning (ACRL) employ a…