2 papers
cs.RO2024
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong +5
Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of dr…
cs.AI2020
World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly C. Stadie
Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence. While deep reinforce…