A forward-inverse dynamic game framework with state-dependent KL regularization and a scene-aware network for inferring agent cost weights from demonstrations.
Open-loop and feedback nash trajectories for competitive racing with ilqgames,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2026 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning
A forward-inverse dynamic game framework with state-dependent KL regularization and a scene-aware network for inferring agent cost weights from demonstrations.