3 papers
cs.LG2026
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…
cs.LG2025
Provably Efficient Sample Complexity for Robust CMDP
Sourav Ganguly, Arnob Ghosh
We study the problem of learning policies that maximize cumulative reward while satisfying safety constraints, even when the real environment differs from a simulator or nominal mo…
cs.LG2025
Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees
Sourav Ganguly, Kishan Panaganti, Arnob Ghosh +1
Constrained decision-making is essential for designing safe policies in real-world control systems, yet simulated environments often fail to capture real-world adversities. We cons…