Reinforcement Learning

All Publication Topics Autonomous Driving Autonomous Exploration Benchmarks Compression Computer Vision First-Person Video Forecasting Generative Models Imitation Learning Intrinsic Control Machine Learning Offroad Navigation Paper Award Planning Reinforcement Learning Reward Learning Robotics

DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation

James R. Han, Hugues Thomas, Jian Zhang, Nicholas Rhinehart, Timothy D. Barfoot.
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation.
arXiv preprint arXiv:2410.10646, (arXiv), 2024.

Computer Vision Forecasting Machine Learning Robotics Reinforcement Learning

Offline reinforcement learning for visual navigation

Dhruv Shah, Arjun Bhorkar, Hrish Leen, Ilya Kostrikov, Nicholas Rhinehart, Sergey Levine.
Offline reinforcement learning for visual navigation.
Conference on Robot Learning, (CoRL), 2022.

Computer Vision Machine Learning Offroad Navigation Reinforcement Learning Robotics

Information is power: Intrinsic control via information capture

Nicholas Rhinehart, Jenny Wang, Glen Berseth, John Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine.
Information is power: Intrinsic control via information capture.
Advances in Neural Information Processing Systems, (NeurIPS), 2021.

Autonomous Exploration Intrinsic Control Generative Models Reinforcement Learning

SMiRL: Surprise Minimizing RL in Dynamic Environments

Glen Berseth, Daniel Geng, Coline Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine.
SMiRL: Surprise Minimizing RL in Dynamic Environments.
International Conference on Representation Learning, (ICLR), 2019.

Reinforcement Learning Intrinsic Control Machine Learning Autonomous Exploration

Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data

Nicholas Rhinehart.
Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data.
2019.

Autonomous Driving Forecasting Generative Models Imitation Learning Planning Reinforcement Learning Reward Learning

N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning

Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani.
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning.
International Conference on Learning Representations, (ICLR), 2018.

Compression Machine Learning Reinforcement Learning

Predictive-state decoders: Encoding the future into recurrent networks

Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. A. Bagnell.
Predictive-state decoders: Encoding the future into recurrent networks.
Advances in Neural Information Processing Systems, (NeurIPS), 2017.

Imitation Learning Forecasting Machine Learning Reinforcement Learning