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Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning
Janaka Chathuranga Brahmanage, Akshat Kumar
Sequential decision making using Markov Decision Process underpins many realworld applications. Both model-based and model free methods have achieved strong results in these settin…
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…
Offline Safe Reinforcement Learning Using Trajectory Classification
Ze Gong, Akshat Kumar, Pradeep Varakantham
Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most ex…
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints
Siow Meng Low, Akshat Kumar
In safe Reinforcement Learning (RL), safety cost is typically defined as a function dependent on the immediate state and actions. In practice, safety constraints can often be non-M…
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar
Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical and resource-allocation related decision making problems. A major challenge in AC…