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.
MDP Planning as Policy Inference
arXiv:2602. 17375v3 Announce Type: replace Abstract: We formulate episodic Markov decision process (MDP) planning as Bayesian inference over policies.
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arXiv AI
Jun 2
Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
arXiv:2606. 00680v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets.
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).
Hugging Face Trending Papers
Aug 26
Quantitative Analysis of $ω$-Regular Robust MDPs
Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consider $(s,a)$-rectangu...
arXiv Machine Learning
1d ago
Towards Optimal Policy Improvement
The paper introduces a framework for optimal policy improvement in reinforcement learning, defining it as the best single update under given constraints. It shows that restricting improvement to a subset of states is equivalent to solving an induced Markov Decision Process, linking planning with explicit or implicit models to optimal policy improvement. The authors develop a novel operator for greedification under approximate evaluation, demonstrating empirical gains across several RL algorithms and settings.
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...
Hugging Face Trending Papers
Jun 23
Solving Markov Decision Processes with Future Information via MPC
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision-making problems formulated as Markov Decision Processes (MDPs).
Hugging Face Trending Papers
Aug 3
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards.
arXiv Machine Learning
Jul 17
A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models
arXiv:2607. 14522v1 Announce Type: new Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC).
arXiv AI
Jul 23
Sophisticated Policies from Epistemic Priors
arXiv:2607. 19518v1 Announce Type: new Abstract: Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search.
arXiv AI
4d ago
Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
arXiv:2609.35874v1 Announce Type: new Abstract: Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost state...
arXiv AI
Jun 30
What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertainty
arXiv:2603. 02491v3 Announce Type: replace-cross Abstract: As artificial agents become increasingly capable, what internal structure is necessary for an agent to act competently under uncertainty?