Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

Taylor Killian; Samuel Daulton; George Konidaris; Finale Doshi-Velez

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arXiv · 1706.06544

Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

Taylor Killian·Samuel Daulton·George Konidaris·Finale Doshi-Velez

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Abstract

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.

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BibTeX RIS

Taylor Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez. 2017-06-20. Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes. https://arxiv.org/abs/1706.06544

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