SOTAVerified

Safe Exploration

Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 41–50 of 135 papers

TitleStatusHype
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning—0
Learning Human-like Representations to Enable Learning Human Values—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
A safe exploration approach to constrained Markov decision processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Reinforcement Learning by Guided Safe Exploration—0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)Code0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Show:102550
← PrevPage 5 of 14Next →

No leaderboard results yet.