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.
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.
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arXiv Machine Learning
Jul 9
Safe Reinforcement Learning using Ideas from Model Predictive Control
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
arXiv AI
Jun 10
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.
Hugging Face Trending Papers
Jul 8
Safe Reinforcement Learning using Ideas from Model Predictive Control
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
arXiv AI
Jul 17
SMC-ES: Automated synthesis of formally verified control policies
arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.
arXiv Machine Learning
Jun 15
Provably Safe, Yet Scalable Reinforcement Learning
arXiv:2606. 14536v1 Announce Type: new Abstract: Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints.
arXiv Machine Learning
Jul 1
Safe Online Learning via Smooth Safety-Structured Policy Composition
arXiv:2606. 31320v1 Announce Type: new Abstract: Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics.
arXiv AI
Jun 19
Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation
arXiv:2606. 19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets.
arXiv Machine Learning
Jun 25
Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning
arXiv:2606. 25357v1 Announce Type: new Abstract: State abstraction plays a key role in scaling reinforcement learning to complex but structured systems.
arXiv AI
Jul 10
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
arXiv:2603. 15136v2 Announce Type: replace-cross Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints.
arXiv Machine Learning
Jun 16
On the Role of Computation in Reinforcement Learning
arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?
arXiv AI
Jun 24
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
arXiv:2606. 24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints.