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.
Active Inference as a Convex Markov Decision Process
arXiv:2607. 20152v1 Announce Type: cross Abstract: Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principle.
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arXiv AI
Jul 10
Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition
arXiv:2605. 14982v2 Announce Type: replace-cross Abstract: We address the discounted reward setting in reinforcement learning (RL).
arXiv Machine Learning
Jul 9
Mathematical methods of reinforcement learning
arXiv:2607. 06935v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly grounded in tools from probability, optimization, and operator theory.
arXiv AI
Jun 4
What Type of Inference is Active Inference?
arXiv:2606. 04935v1 Announce Type: new Abstract: Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior.
arXiv AI
Jun 10
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.
arXiv AI
Jun 19
Can In-Context Learning Support Intrinsic Curiosity?
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
arXiv AI
Jun 30
Exploration and Online Transfer with Behavioral Foundation Models
arXiv:2606. 29980v1 Announce Type: new Abstract: Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional learning at transfer time, while training only on reward-free trajectories.
arXiv AI
Aug 11
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning
arXiv:2608. 08158v1 Announce Type: new Abstract: Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning.
arXiv AI
Jun 2
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
arXiv:2606. 00367v1 Announce Type: cross Abstract: Reinforcement learning problems typically define the goal as maximizing the expected value of a scalar reward function.
arXiv Machine Learning
Jun 25
Auto-exploration for online reinforcement learning
arXiv:2512. 06244v2 Announce Type: replace Abstract: The exploration-exploitation dilemma in reinforcement learning (RL) is a fundamental challenge to efficient RL algorithms.
arXiv AI
Jul 3
Full Bayesian Reinforcement Learning via LF-IBIS
arXiv:2607. 01741v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rewards.
arXiv Machine Learning
Jun 30
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.