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Oral
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
in Reinforcement Learning, Algorithms, Applications »

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)
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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