arXiv AI
Jul 13
Risk-Aware General-Utility Markov Decision Processes
arXiv:2607. 09298v1 Announce Type: cross Abstract: We study general-utility Markov decision processes (GUMDPs) with risk-aware objectives.
Long-Term Sequential Decision Making under Risk
arXiv:2607. 19914v1 Announce Type: new Abstract: We study finite-horizon MDP planning under \emph{root-based} (resolute) risk objectives that apply a rank-dependent functional to the distribution of total returns.
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arXiv AI
Jul 1
Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
arXiv:2602. 03778v2 Announce Type: replace-cross Abstract: Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events.
arXiv AI
Aug 11
Control-Oriented Scenario Tree Construction through Reinforcement Learning
arXiv:2608. 09335v1 Announce Type: new Abstract: Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts.
Hugging Face Trending Papers
Aug 10
Control-Oriented Scenario Tree Construction through Reinforcement Learning
Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts. To build such a tree, conventional methods focus on matching the underlying probability distribution---e.
arXiv Machine Learning
Jun 19
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
arXiv:2606. 20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration.
arXiv Machine Learning
Jul 1
Policy Optimization Achieves Data-Dependent Regret Bounds in MDPs with Unknown Transitions
arXiv:2606. 31769v1 Announce Type: new Abstract: We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dependent regret bounds.
arXiv AI
Jul 7
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
arXiv:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
arXiv AI
Jun 29
RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance
arXiv:2606. 27766v1 Announce Type: cross Abstract: Offline reinforcement learning enables policy learning from fixed datasets without additional environment interaction, making it appealing for safety-critical applications where online exploration is costly or unsafe.
Hugging Face Trending Papers
Jul 6
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially with lookahead depth in the worst case.
arXiv Machine Learning
Jun 4
Bayesian learning for the stochastic shortest path problem
arXiv:2606. 04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP).
arXiv AI
Jun 18
Robust Regularized Policy Iteration under Transition Uncertainty
arXiv:2603. 09344v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.
arXiv Machine Learning
Jul 20
Robust Peak-cost Constrained Reinforcement Learning
arXiv:2607. 15457v1 Announce Type: new Abstract: We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a trajectory.