arXiv AI
Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
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arXiv Machine Learning
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Learning Chance-Constrained MDPs with Bellman Distributional Certificates
The paper introduces a new approach to learning chance-constrained Markov decision processes (CCMDPs) using a Bellman distributional certificate. It provides both model-based and model-free algorithms with theoretical guarantees, including matching upper and lower bounds for tabular discounted CCMDPs with bounded successor support. Numerical experiments on synthetic CCMDPs and an IEEE 14-bus energy storage benchmark demonstrate the safety and effectiveness of the proposed methods.
arXiv AI
Jun 11
Planning under Distribution Shifts with Causal POMDPs
arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.
arXiv AI
Jul 21
Expected Free Energy as Belief-Dependent Utility for rho-POMDPs
arXiv:2607. 16981v1 Announce Type: new Abstract: An agent acting under partial observability must decide when to gather information and which observations are worth their cost.
arXiv AI
Jun 4
Vectorized Online POMDP Planning
arXiv:2510. 27191v5 Announce Type: replace-cross Abstract: Planning under partial observability is an essential capability of autonomous robots.
arXiv Machine Learning
Aug 26
Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs
arXiv:2604.01024v2 Announce Type: replace Abstract: We study model-based learning of finite-window policies in tabular partially observable Markov decision processes (POMDPs). A common approach to le...
arXiv Machine Learning
Jun 4
Policy Gradient for Continuous-Time Robust Markov Decision Processes
arXiv:2606. 04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics.