no code implementations • 11 Oct 2023 • Yuxin Chen, Chen Tang, Ran Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, Wei Zhan
We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.
no code implementations • 18 Sep 2023 • Yiheng Li, Seth Z. Zhao, Chenfeng Xu, Chen Tang, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan
We propose to augment both HD maps and trajectories and apply pre-training strategies on top of them.
no code implementations • NeurIPS 2023 • Chenran Li, Chen Tang, Haruki Nishimura, Jean Mercat, Masayoshi Tomizuka, Wei Zhan
Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.
no code implementations • 24 Mar 2023 • Wei-Jer Chang, Chen Tang, Chenran Li, Yeping Hu, Masayoshi Tomizuka, Wei Zhan
To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment.
no code implementations • 9 Aug 2022 • Wei-Jer Chang, Yeping Hu, Chenran Li, Wei Zhan, Masayoshi Tomizuka
In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.