Papers Safe Reinforcement Learning
“Safe Reinforcement Learning” 태그가 달린 논문 306편 · 필터 해제
Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
Existing approaches to language model alignment often treat safety as a tradeoff against helpfulness, which can lead to unacceptable responses in sensitive domains. To ensure reliable performance in such settings, we pro…
Safe Reinforcement LearningProvably Safe Reinforcement Learning from Analytic Gradients
Deploying autonomous robots in safety-critical applications requires safety guarantees. Provably safe reinforcement learning is an active field of research which aims to provide such guarantees using safeguards. These sa…
reinforcement-learningReinforcement LearningSafe Reinforcement LearningA Provable Approach for End-to-End Safe Reinforcement Learning
A longstanding goal in safe reinforcement learning (RL) is a method to ensure the safety of a policy throughout the entire process, from learning to operation. However, existing safe RL paradigms inherently struggle to a…
Gaussian ProcessesReinforcement Learning (RL)Safe Reinforcement LearningOffline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clinical expertise can result in potentially …
Offline RLQ-Learningreinforcement-learningReinforcement Learning+2Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems
This paper presents a risk-aware safe reinforcement learning (RL) control design for stochastic discrete-time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk-in…
Reinforcement Learning (RL)Safe Reinforcement LearningFeasibility-Aware Pessimistic Estimation: Toward Long-Horizon Safety in Offline RL
Offline safe reinforcement learning(OSRL) derives constraint-satisfying policies from pre-collected datasets, offers a promising avenue for deploying RL in safety-critical real-world domains such as robotics. However, th…
Offline RLSafe Reinforcement LearningSkill-based Safe Reinforcement Learning with Risk Planning
Safe Reinforcement Learning (Safe RL) aims to ensure safety when an RL agent conducts learning by interacting with real-world environments where improper actions can induce high costs or lead to severe consequences. In t…
reinforcement-learningReinforcement LearningSafe Reinforcement LearningDesigning Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control framework leveraging neural network verific…
Autonomous NavigationSafe Reinforcement LearningAnytime Safe Reinforcement Learning
This paper considers the problem of solving constrained reinforcement learning problems with anytime guarantees, meaning that the algorithmic solution returns a safe policy regardless of when it is terminated. Drawing in…
reinforcement-learningReinforcement LearningSafe Reinforcement LearningTraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback
In safe reinforcement learning (RL), auxiliary safety costs are used to align the agent to safe decision making. In practice, safety constraints, including cost functions and budgets, are unknown or hard to specify, as i…
continuous-controlContinuous ControlDecision MakingReinforcement Learning (RL)+1Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agents
Generalizable alignment is a core challenge for deploying Large Language Models (LLMs) safely in real-world NLP applications. Current alignment methods, including Reinforcement Learning from Human Feedback (RLHF), often …
Safe Reinforcement LearningSafety Modulation: Enhancing Safety in Reinforcement Learning through Cost-Modulated Rewards
Safe Reinforcement Learning (Safe RL) aims to train an RL agent to maximize its performance in real-world environments while adhering to safety constraints, as exceeding safety violation limits can result in severe conse…
Safe Reinforcement LearningBresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-the-art safe RL approaches mitigate risk…
Reinforcement Learning (RL)Safe ExplorationSafe Reinforcement LearningSafe RLHF-V: Safe Reinforcement Learning from Human Feedback in Multimodal Large Language Models
Multimodal large language models (MLLMs) are critical for developing general-purpose AI assistants, yet they face growing safety risks. How can we ensure that MLLMs are safely aligned to prevent undesired behaviors such …
MisinformationSafe Reinforcement LearningSafety AlignmentReachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR
The driving risk field is applicable to more complex driving scenarios, providing new approaches for safety decision-making and active vehicle control in intricate environments. However, existing research often overlooks…
reinforcement-learningReinforcement LearningSafe Reinforcement LearningTrajectory PlanningHierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation
Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing…
Hierarchical Reinforcement LearningMotion Planningreinforcement-learningReinforcement Learning+1Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning
In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-function from the related environments to pre…
Contrastive LearningRepresentation LearningSafe Reinforcement LearningHASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans primarily rely on embodied visual perceptio…
NavigateReinforcement Learning (RL)Safe Reinforcement LearningProbabilistic Shielding for Safe Reinforcement Learning
In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximise their reward, must often also behave in a safe manner, including at training time. Thus, much attention in recent years has been given to Saf…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement LearningSafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robo…
Safe Reinforcement LearningSafety AlignmentVision-Language-Action