Poster
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris
Pacific Ballroom #36
Keywords: [ Markov Decision Processes ] [ Model-Based RL ] [ Reinforcement Learning ] [ Latent Variable Models ] [ Reinforcement Learning and Planning ] [ Multitask and Transfer Learning ]
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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