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 Manipulation Model-Based Control Offroad Navigation Paper Award Reinforcement Learning Reward Learning Robotics Social Navigation
Residual Reward Models for Preference-based Reinforcement Learning
Chenyang Cao, Miguel Rogel-García, Mohamed Nabail, Xueqian Wang, Nicholas Rhinehart.
Residual Reward Models for Preference-based Reinforcement Learning.
arXiv:2507.00611, (arXiv), 2025.
PDF Cite
Reward Learning Machine Learning Robotics Reinforcement Learning Manipulation
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.
IEEE Robotics and Automation Letters, (RA-L), 2025.
PDF Cite
Computer Vision Forecasting Machine Learning Model-Based Control Robotics Reinforcement Learning Social Navigation
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.
PDF Cite
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.
PDF Cite
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.
PDF Cite
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.
PDF Cite Project
Autonomous Driving Forecasting Generative Models Imitation Learning Model-Based Control 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.
PDF Cite
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.
PDF Cite
Imitation Learning Forecasting Machine Learning Reinforcement Learning