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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 04:35 PM -- 04:50 PM (PST) @ Hall A

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. We replace the original Gaussian Process-based model with a Bayesian Neural Network. Our new framework correctly models the joint uncertainty in the latent weights and the state space and has more scalable inference, thus expanding the scope the HiP-MDP to applications with higher dimensions and more complex dynamics.

Author Information

Taylor Killian (Harvard University)
Sam Daulton (Facebook)
Finale Doshi-Velez (Harvard)
George Konidaris (Brown University)

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