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Poster
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.
Author Information
Taylor Killian (Harvard University)
Samuel Daulton (Facebook)
Research Scientist at Meta, PhD Candidate at Oxford. My research focuses on Bayesian optimization.
Finale Doshi-Velez (Harvard)
George Konidaris (Brown University)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Oral: Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes »
Thu. Dec 7th 12:35 -- 12:50 AM Room Hall A
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