Image de couverture de livre

Provably Safe Reinforcement Learning for Stochastic Reach-Avoid Problems with Entropy Regularization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Mazumdar, Abhijit, Wisniewski, Rafal, Bujorianu, Manuela L.
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
par: Mazumdar, Abhijit, et autres
Publié: (2024)

Data-Driven Robust Safety Verification for Markov Decision Processes
par: Mazumdar, Abhijit, et autres
Publié: (2025)

Robust Correlated Equilibrium: Definition and Computation
par: Misra, Rahul, et autres
Publié: (2023)

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
par: Pan, Jingduo, et autres
Publié: (2026)

An Online Multiobjective Policy Gradient for Long-run Average-reward Markov Decision Process
par: Misra, Rahul, et autres
Publié: (2025)

Large Deviations in Safety-Critical Systems with Probabilistic Initial Conditions
par: Gomez, Aitor R., et autres
Publié: (2024)

Solving Minimum-Cost Reach Avoid using Reinforcement Learning
par: So, Oswin, et autres
Publié: (2024)

Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning
par: Zhao, Weiye, et autres
Publié: (2024)

SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
par: Anisimov, Maksim, et autres
Publié: (2026)

Distributionally Robust Safety Verification for Markov Decision Processes
par: Mazumdar, Abhijit, et autres
Publié: (2024)

Leveraging Analytic Gradients in Provably Safe Reinforcement Learning
par: Walter, Tim, et autres
Publié: (2025)

Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
par: Banerjee, Arko, et autres
Publié: (2024)

Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
par: Hoang, Huy, et autres
Publié: (2023)

State Entropy Regularization for Robust Reinforcement Learning
par: Ashlag, Yonatan, et autres
Publié: (2025)

Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
par: Krasowski, Hanna, et autres
Publié: (2024)

Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients
par: Chenevert, Gabriel, et autres
Publié: (2024)

Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference
par: Liu, Yizhi
Publié: (2026)

Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
par: Li, Jingqi, et autres
Publié: (2022)

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
par: Jhaveri, Yash, et autres
Publié: (2025)

A Provable Approach for End-to-End Safe Reinforcement Learning
par: Wachi, Akifumi, et autres
Publié: (2025)

The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
par: Sims, Anya, et autres
Publié: (2024)

Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems
par: Tian, Haozhe, et autres
Publié: (2024)

Provably Safe Model Updates
par: Elmecker-Plakolm, Leo, et autres
Publié: (2025)

Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning
par: Nakhaei, Mohammadreza, et autres
Publié: (2024)

Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning
par: Ghanem, Abdelghani, et autres
Publié: (2026)

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
par: Zhu, Zhaoyu, et autres
Publié: (2026)

Provably Convergent Actor-Critic for MARL through Risk-aversion
par: Zhang, Yizhou, et autres
Publié: (2026)

Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning
par: So, Oswin, et autres
Publié: (2026)

Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
par: Massiani, Pierre-François, et autres
Publié: (2025)

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
par: Ramanujam, Asha, et autres
Publié: (2025)

Towards Provable Emergence of In-Context Reinforcement Learning
par: Wang, Jiuqi, et autres
Publié: (2025)

Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies
par: Li, Kevin, et autres
Publié: (2025)

Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
par: Suh, Jihoon, et autres
Publié: (2025)

Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem
par: Guin, Soumyajit, et autres
Publié: (2025)

Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning
par: Liu, Bing, et autres
Publié: (2025)

Model-Free Robust $ϕ$-Divergence Reinforcement Learning Using Both Offline and Online Data
par: Panaganti, Kishan, et autres
Publié: (2024)

Safely Learning Controlled Stochastic Dynamics
par: Brogat-Motte, Luc, et autres
Publié: (2025)

Provable Partially Observable Reinforcement Learning with Privileged Information
par: Cai, Yang, et autres
Publié: (2024)

Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties
par: Arndt, Clemens, et autres
Publié: (2024)

Stabilizing Information Flow Entropy: Regularization for Safe and Interpretable Autonomous Driving Perception
par: Yang, Haobo, et autres
Publié: (2025)