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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

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Showing 71–80 of 135 papers

Title	Date	Tasks	Status	Hype
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning	Feb 15, 2022	reinforcement-learningReinforcement Learning	CodeCode Available	0
Model-Based Offline Meta-Reinforcement Learning with Regularization	Feb 7, 2022	Meta Reinforcement Learningreinforcement-learning	—Unverified	0
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints	Jan 28, 2022	Reinforcement Learning (RL)Safe Exploration	—Unverified	0
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems	Jan 24, 2022	global-optimizationSafe Exploration	CodeCode Available	0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance	Dec 7, 2021	continuous-controlContinuous Control	—Unverified	0
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning	Dec 1, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0
Safe Policy Optimization with Local Generalized Linear Function Approximations	Nov 9, 2021	Reinforcement Learning (RL)Safe Exploration	CodeCode Available	0
Infinite Time Horizon Safety of Bayesian Neural Networks	Nov 4, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention	Oct 27, 2021	OpenAI Gymreinforcement-learning	—Unverified	0
Dual-Arm Adversarial Robot Learning	Oct 15, 2021	Safe Exploration	—Unverified	0

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