Search Results for author: John Dolan

Found 12 papers, 4 papers with code

Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving

no code implementations12 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.

Autonomous Driving Trajectory Forecasting

Safe Deep Policy Adaptation

1 code implementation8 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.

reinforcement-learning Reinforcement Learning (RL) +1

Reasoning with Latent Diffusion in Offline Reinforcement Learning

1 code implementation12 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.

D4RL Offline RL +3

Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning

no code implementations13 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

Safe Control Under Input Limits with Neural Control Barrier Functions

1 code implementation20 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.

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

no code implementations21 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.

Autonomous Driving D4RL +2

Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization

no code implementations1 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.

D4RL reinforcement-learning +1

Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data

no code implementations29 Oct 2019 Qin Lin, Sicco Verwer, John Dolan

Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data.

Autonomous Vehicles Imitation Learning

Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

no code implementations28 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.

Autonomous Vehicles Clustering

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

no code implementations9 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.

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