safe exploration

Enhancing Safe Exploration Using Safety State Augmentation NeurIPS 2022 Information-Theoretic Safe Exploration with Gaussian Processes NeurIPS 2022 Near-Optimal Multi-Agent Learning for Safe Coverage Control NeurIPS 2022 Safe Exploration for Efficient Policy Evaluation and Comparison ICML 2022 Safe Optimal Design with Applications in Off-Policy Learning AISTATS 2022 Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk ICML 2022 Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models JMLR 2022 Conservative Safety Critics for Exploration ICLR 2021 Dual-Arm Adversarial Robot Learning CoRL 2021 Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs NeurIPS 2021 Provably Efficient Safe Exploration via Primal-Dual Policy Optimization AISTATS 2021 Provably Safe PAC-MDP Exploration Using Analogies AISTATS 2021 Robot Reinforcement Learning on the Constraint Manifold CoRL 2021 Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach L4DC 2021 Safe Policy Optimization with Local Generalized Linear Function Approximations NeurIPS 2021 Uncertain-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric L4DC 2021 Avoiding Side Effects in Complex Environments NeurIPS 2020 Constrained Markov Decision Processes via Backward Value Functions ICML 2020 Constrained Upper Confidence Reinforcement Learning L4DC 2020 Learning from Interventions Using Hierarchical Policies for Safe Learning AAAI 2020 Learning Human Objectives by Evaluating Hypothetical Behavior ICML 2020 Neurosymbolic Reinforcement Learning with Formally Verified Exploration NeurIPS 2020 Pessimism About Unknown Unknowns Inspires Conservatism COLT 2020 Robust Regression for Safe Exploration in Control L4DC 2020 Safe Linear Stochastic Bandits AAAI 2020