Model Learning
←All ResearchStructured tools for predicting into the future
Modeling learning is the process of taking logged data and utilizing it to create a tool for predicting into the future. My work here started in the area of model-based reinforcement learning, but it is broad enough now that it warrants its own category. Model learning from batch data is also of great interest. If we can learn a useful model, we can leverage all the data we have logged to its fullest extent.
Open areas of study:
- Models for long-term predictions,
- Changing model training to prioritize task performance over accuracy,
- Model predictions with multi-modal data,
- Applications of low-data model learning.
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Feb 26, 2021
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2021 International Conference on Artificial Intelligence and Statistics
We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.
Objective Mismatch in Model-based Reinforcement Learning
Feb 11, 2020
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2020 Conference on Learning for Decision and Control
Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning -- control and dynamics. When optimizing model accuracy, there is no guarantee on improving task performance!
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
Jan 11, 2019
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IEEE Robotics and Automation Letters
We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.