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Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #36

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)

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