arXiv Machine Learning
Jun 4
Explainably Safe Reinforcement Learning
arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.
Robust Shielding for Safe Reinforcement Learning
arXiv:2606. 00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs).
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arXiv Machine Learning
Jun 15
Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning
arXiv:2606. 14130v1 Announce Type: new Abstract: Safe coordination problems surface in multi-agent reinforcement learning when global safety cannot be enforced by any agent unilaterally: the admissibility of one agent's action may depend on the dynamics of other agents.
arXiv Machine Learning
Aug 4
Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives
arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.
arXiv AI
Jun 16
Safe Exploration via Policy Priors
arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.
arXiv AI
Jul 21
A Survey on the Verification of Reinforcement Learning Policies
arXiv:2607. 16210v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly applied in complex, safety-critical domains, yet the lack of rigorous behavioral guarantees for neural network-based policies remains a major barrier to deployment.
arXiv Machine Learning
Jul 21
Theoretical Foundations of $\max$@$k$ Reinforcement Learning
arXiv:2607. 17823v1 Announce Type: new Abstract: Reinforcement Learning is a cornerstone technique for modern large reasoning models.
arXiv AI
Jun 4
Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
arXiv:2606. 04812v1 Announce Type: cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour.
arXiv Machine Learning
Jun 3
Easy-to-Use Shielding for Reinforcement Learning
arXiv:2606. 03804v1 Announce Type: new Abstract: Safe exploration is a key challenge in Reinforcement Learning (RL) that aims to prevent agents from making harmful decisions while exploring their environment.
arXiv Machine Learning
Aug 3
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
arXiv:2307. 10524v3 Announce Type: replace Abstract: We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice.
arXiv Machine Learning
Jul 1
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
arXiv Machine Learning
Jul 7
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
arXiv:2607. 03168v1 Announce Type: cross Abstract: Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigorous theoretical foundation.
arXiv Machine Learning
1d ago
Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments
arXiv:2603. 06946v2 Announce Type: replace Abstract: Many distributional quantities in reinforcement learning are intrinsically joint across actions, including distributions of gaps and probabilities of superiority.