arXiv AI

Interval POMDP Shielding for Imperfect-Perception Agents

arXiv:2604. 20728v2 Announce Type: replace Abstract: Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified.

arXiv AI

Jun 2

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).

By Edwin Hamel-De le Court, Thom Badings, Alessandro Abate, Francesco Belardinelli, Francesco Fabiano

agents reinforcement-learning safety

arXiv AI

19h ago

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning

arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).

By Astrid Horn Brorholt (Aalborg University, Aalborg, Denmark), Maris F. L. Galesloot (Radboud University, Nijmegen, Netherlands), Nils Jansen (Radboud University, Nijmegen, Netherlands), Kim Guldstrand Larsen (Aalborg University, Aalborg, Denmark), Christian Schilling (Aalborg University, Aalborg, Denmark)

agents reinforcement-learning safety

arXiv AI

Jun 11

Planning under Distribution Shifts with Causal POMDPs

arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.

By Matteo Ceriscioli, Karthika Mohan

reinforcement-learning

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.

By Sabine Rieder, Stefan Pranger, Debraj Chakraborty, Jan K\v{r}et\'insk\'y, Bettina K\"onighofer

reinforcement-learning safety

arXiv AI

Jun 2

Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

arXiv:2606. 02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate.

By Haimin Hu

agents robotics benchmarks safety

arXiv Machine Learning

Jun 2

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe

agents reinforcement-learning robotics

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.

By Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman

reinforcement-learning

arXiv AI

Jul 22

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.

By Seiji Shaw, Travis Manderson, Chad Kessens, Nicholas Roy

agents reinforcement-learning

arXiv AI

Jul 7

Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making

arXiv:2607. 04019v1 Announce Type: cross Abstract: Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use.

By Hyung-Jin Yoon, Hunmin Kim

arXiv Machine Learning

Jun 15

PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data

arXiv:2507. 20068v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment.

By Aishwarya Mandyam, Jason Meng, Ge Gao, Jiankai Sun, Mac Schwager, Barbara E. Engelhardt, Emma Brunskill

reinforcement-learning robotics

arXiv AI

Jul 17

Decision Making Needs Uncertainty Quantification [Lecture Notes]

arXiv:2607. 14407v1 Announce Type: cross Abstract: Many signal processing systems ultimately exist to {act}.

By Osvaldo Simeone

agents

arXiv Machine Learning

Aug 11

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.

By Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe

reinforcement-learning benchmarks safety

Related stories