|Stochastic Systems Group|
Hierarchical Bayesian Methods for Reinforcement Learning
Computational Cognitive Science, MIT
Designing autonomous agents capable of coping with the complexity of the real world is a tremendous engineering challenge. Such agents must often deal with rich observations (such as images), unknown dynamics, and rich structure---perhaps consisting of objects, their properties/types and their dynamical interactions. An ability to learn from experience and generalize radically to new situations is essential; at the same time, the agent may bring substantial prior knowledge to bear on the environment it finds itself in.
In this talk, I will present recent work on the combination of reinforcement learning and nonparametric Bayesian modeling. Hierarchical Bayes provides a principled framework for incorporating prior knowledge and dealing explicitly with uncertainty, while reinforcement learning provides a framework for making sequential decisions under uncertainty. I will discuss how nonparametric Bayesian models can help answer two questions: 1) how can an agent learn a representation of state space in a structured domain? and 2) how can an agent learn how to search for good control laws in hard-to-search spaces?
I will illustrate the concepts on applications including modeling neural spike train data, causal sound source separation and optimal control in high-dimensional, simulated robotic environments.
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