no code implementations • 12 Mar 2024 • Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John Dolan, Jeff Schneider
Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining.
1 code implementation • 8 Oct 2023 • Wenli Xiao, Tairan He, John Dolan, Guanya Shi
In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges.
1 code implementation • 12 Sep 2023 • Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth
However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset.
no code implementations • 13 Apr 2023 • Wenli Xiao, Yiwei Lyu, John Dolan
This design enables efficient synthesis of shields to monitor agents in complex environments without coordination overheads.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 1 Dec 2022 • Soumith Udatha, Yiwei Lyu, John Dolan
We use probabilistic control barrier functions as an estimate of the model uncertainty.
1 code implementation • 20 Nov 2022 • Simin Liu, Changliu Liu, John Dolan
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations.
no code implementations • 21 Jul 2022 • Adam Villaflor, Zhe Huang, Swapnil Pande, John Dolan, Jeff Schneider
Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem.
1 code implementation • CVPR 2021 • Peiyun Hu, Aaron Huang, John Dolan, David Held, Deva Ramanan
Finally, we propose future freespace as an additional source of annotation-free supervision.
no code implementations • 1 Jan 2021 • Adam Villaflor, John Dolan, Jeff Schneider
Then, we can optionally enter a second stage where we fine-tune the policy using our novel Model-Based Behavior-Regularized Policy Optimization (MB2PO) algorithm.
no code implementations • 29 Oct 2019 • Qin Lin, Sicco Verwer, John Dolan
Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data.
no code implementations • 28 Oct 2019 • Qin Lin, Wenshuo Wang, Yihuan Zhang, John Dolan
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic environment.
no code implementations • 9 Aug 2014 • Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John Dolan, Gaurav Sukhatme
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots.