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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

TitleStatusHype
Effects of Safety State Augmentation on Safe Exploration—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing—0
Exploration in Deep Reinforcement Learning: A Survey—0
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Safe Exploration for Efficient Policy Evaluation and Comparison—0
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement LearningCode0
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