6 papers · 1 filter
Online Policy Evaluation for MDPs with Dynamic UBSR Measures
Weikai Wang, Erick Delage
Developing efficient function-approximation methods for policy evaluation is a fundamental challenge in risk-aware reinforcement learning. Existing approaches either focus on restr…
Planning and Learning in Average Risk-aware MDPs
Weikai Wang, Erick Delage
For continuing tasks, average cost Markov decision processes have well-documented value and can be solved using efficient algorithms. However, it explicitly assumes that the agent…
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
Jaike van Twiller, Yossiri Adulyasak, Erick Delage +2
Reinforcement learning (RL) has successfully solved various deterministic and stochastic planning problems. However, conventional RL struggles with complex real-world constraints,…
Fair Resource Allocation in Weakly Coupled Markov Decision Processes
Xiaohui Tu, Yossiri Adulyasak, Nima Akbarzadeh +1
We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where resource constraints couple the action sp…
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…
Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning
Nima Akbarzadeh, Yossiri Adulyasak, Erick Delage
In restless bandits, a central agent is tasked with optimally distributing limited resources across several bandits (arms), with each arm being a Markov decision process. In this w…