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End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

AAAI 2019 · Reinforcement learning › Policy gradient and actor-critic · approach: Safety layers and shielding · 575 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add CBF safety filtering and online dynamics learningTRPO · Inverted pendulum (pendulum-v0)learning efficiencyHelped
add CBF safety filtering and online dynamics learningDDPG · Inverted pendulum (pendulum-v0)learning efficiencyHelped
add CBF safety filtering and online dynamics learningTRPO · Inverted pendulum (pendulum-v0)safetyHelped
add CBF safety filtering and online dynamics learningDDPG · Inverted pendulum (pendulum-v0)safetyHelped
add CBF safety filtering and online dynamics learningTRPO · Simulated car followinglearning efficiencyHelped
add CBF safety filtering and online dynamics learningDDPG · Simulated car followingsafetyHelped
add CBF safety filtering and online dynamics learningTRPO · Simulated car followingsafetyHelped
add CBF safety filtering and online dynamics learningDDPG · Simulated car followinglearning efficiencyHelped
use barrier-guided updates around the previously deployed controller rather than barrier compensation aloneRL-CBF · Policy explorationlearning efficiencyHelped

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