ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning

The recent submission titled 'ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning' explores the critical issue of distributional mismatch in reinforcement learning (RL), where policies trained in simulators often fail in real-world applications due to differing conditions. This research introduces a more practical framework for online distributionally robust RL, allowing agents to interact with a single unknown training environment while ensuring robustness against uncertainties. By utilizing general f-divergence-based ambiguity sets, including chi-squared and KL divergence, the study aims to establish a minimax lower bound on the regret of any online algorithm, thereby enhancing the reliability of RL systems in unpredictable settings. The significance of this work lies in its potential to provide essential guarantees on real-world performance, addressing a major limitation in existing RL methodologies and paving the way for more effective deployment of RL technol…