5 papers
Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals
Guopeng Li, Yiyang Duan, Yiru Jiao +1
Contrastive reinforcement learning (CRL) scales effectively in goal-conditioned tasks by casting policy learning into a self-supervised contrastive objective. However, in a failure…
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
Guopeng Li, Moritz A. Zanger, Matthijs T. J. Spaan +1
Safe robot control requires maximizing return while satisfying safety constraints. In off-policy safe reinforcement learning, reward and safety Q-values are commonly learned by sep…
Off-Policy Safe Reinforcement Learning with Constrained Optimistic Exploration
Guopeng Li, Matthijs T. J. Spaan, Julian F. P. Kooij
When safety is formulated as a limit of cumulative cost, safe reinforcement learning (RL) aims to learn policies that maximize return subject to the cost constraint in data collect…
Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features
Tin T. Nguyen, Simeon C. Calvert, Guopeng Li +1
The fast-growing amount of traffic data brings many opportunities for revealing more insightful information about traffic dynamics. However, it also demands an effective database m…
A Conflict Resolution Dataset Derived from Argoverse-2: Analysis of the Safety and Efficiency Impacts of Autonomous Vehicles at Intersections
Guopeng Li, Yiru Jiao, Simeon C. Calvert +1
As the deployment of autonomous vehicles (AVs) in mixed traffic flow becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and human agents is of critic…