3 papers
cs.LG2025
Risk-Averse Total-Reward Reinforcement Learning
Xihong Su, Jia Lin Hau, Gersi Doko +2
Risk-averse total-reward Markov Decision Processes (MDPs) offer a promising framework for modeling and solving undiscounted infinite-horizon objectives. Existing model-based algori…
cs.LG2025
Efficient Algorithms for Mitigating Uncertainty and Risk in Reinforcement Learning
Xihong Su
This dissertation makes three main contributions. First, We identify a new connection between policy gradient and dynamic programming in MMDPs and propose the Coordinate Ascent Dyn…
cs.LG2024
Risk-averse Total-reward MDPs with ERM and EVaR
Xihong Su, Julien Grand-Clément, Marek Petrik
Optimizing risk-averse objectives in discounted MDPs is challenging because most models do not admit direct dynamic programming equations and require complex history-dependent poli…