2 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.LG2024
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Jia Lin Hau, Erick Delage, Esther Derman +2
In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a standard metric for modeling RL agents' preferences for certain outcomes. This paper propose…