33 citations · 78 across the 12 of their papers we have counts for
6 papers · 1 filter
Investigating Compounding Prediction Errors in Learned Dynamics Models
Nathan Lambert, Kristofer Pister, Roberto Calandra
Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which rel…
The Challenges of Exploration for Offline Reinforcement Learning
Nathan Lambert, Markus Wulfmeier, William Whitney +5
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal…
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
Thomas Krendl Gilbert, Sarah Dean, Tom Zick +1
In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a…
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Baohe Zhang, Raghu Rajan, Luis Pineda +5
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynami…
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Nathan O. Lambert, Albert Wilcox, Howard Zhang +2
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-st…
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan +1
Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focuse…