A reinforcement-learning-based adversary learns to trigger collisions in DRL autonomous-driving policies with about three small, well-timed input perturbations per episode.
On -Ramp Merging for Highway Autonomous Driving: An Application of a New Safety Indicator in Deep Reinforcement Learning,
1 Pith paper cite this work, alongside 33 external citations. Polarity classification is still indexing.
1
Pith paper citing it
33
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies
A reinforcement-learning-based adversary learns to trigger collisions in DRL autonomous-driving policies with about three small, well-timed input perturbations per episode.