Safe Exploration Algorithms for Reinforcement Learning Controllers

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “As one wrong action can result in unrecoverable effects, learning in healthcare domains poses a safety exploration dilemma [324], [32].”
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks (2019, background): “Other works guarantee safety by switching between backup controllers (Perkins and Barto 2003; Mannucci et al. 2018), though this overly constrains policy exploration.”
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning (2021, unknown): “…(119, 120, 121, 122, 123, 124, 125, 128, 129, 130, 133, 134, 135, 136, 139, 140, 142), Parameters (126, 127, 141) Stabilization (128, 136, 139, 141, 142), Tracking (119, 120, 123, 124, 125, 126, 127, 129, 130, 131, 133, 134, 140), Navigation (121, 135), Loco-motion (122) S a f e L e a r n i n g i…”