Model Learning

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Structured 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.

Predicting with a model into the future!

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.
We explored how MBRL can learn multi-step, nonlinear controllers!
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Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!
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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!
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We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.
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