Agents
Language-Driven Interactive Traffic Trajectory Generation
In this work, we propose InteractTraj, the first language-driven traffic trajectory generator that can generate interactive traffic trajectories.
An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning
In this paper, we contribute to both theoretical analyses.
A Diffusion-Model of Joint Interactive Navigation Matthew Niedoba 1,2 J. Wilder Lavington
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors.
Contracting with a Learning Agent Guru Guruganesh Yoav Kolumbus Jon Schneider Inbal T algam-Cohen Emmanouil-V asileios Vlatakis-Gkaragkounis Joshua R. Wang S. Matthew Weinberg
This shift causes the agent to "free-fall" through their
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
Honguk Woo is the corresponding author.
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging.