7 papers
Robust Peak-cost Constrained Reinforcement Learning
Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar +3
We study robust peak-cost constrained reinforcement learning (\ours), where the objective is to maximize expected reward while controlling the maximum cost encountered along a traj…
Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions
Toshinori Kitamura, Arnob Ghosh, Alex Ayoub +2
Projected subgradient descent (PSD) has gained popularity for solving robust Markov decision processes (RMDPs) because it applies to a broader class of uncertainty sets than tradit…
Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
Sourav Ganguly, Kartik Pandit, Arnob Ghosh
Real-world decision-making systems operate in environments where state transitions depend not only on the agent's actions, but also on \textbf{exogenous factors outside its control…
Beyond Freshness and Semantics: A Coupon-Collector Framework for Effective Status Updates
Youssef Ahmed, Arnob Ghosh, Chih-Chun Wang +1
For status update systems operating over unreliable energy-constrained wireless channels, we address Weaver's long-standing Level-C question: do my packets actually improve the pla…
Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici +1
While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel…
Performing Load Balancing under Constraints
Andrea Fox, Francesco De Pellegrini, Eitan Altman +2
Join-the-shortest queue (JSQ) and its variants have often been used in solving load balancing problems. The aim of such policies is to minimize the average system occupation, e.g.,…
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
Honghao Wei, Xiyue Peng, Arnob Ghosh +1
We propose WSAC (Weighted Safe Actor-Critic), a novel algorithm for Safe Offline Reinforcement Learning (RL) under functional approximation, which can robustly optimize policies to…