AIC Seminar Series
Reinforcement Learning by Policy Search
|Leon Peshkin||Harvard University|
Date: Monday, April 5th 2004 at 10:00am
Location: EJ228 (Directions)
Teaching is hard, criticizing is easy. This metaphor stands behind the concept
of reinforcement learning as opposed to supervised learning. Reinforcement
learning means learning a policya mapping of observations into
actionsbased on feedback from the environment. Learning can be viewed as
browsing a set of policies while evaluating them by trial through interaction
with the environment. In this talk I briefly review the framework of
reinforcement learning and present two highlights from my dissertation.
First, I describe an algorithm which learns by ascending the gradient of
expected cumulative reinforcement. I show what conditions enable experience
re-use in learning. Building on statistical learning theory, I address the
question of sufficient experience for uniform convergence of policy
evaluation and obtain sample complexity bounds. Second, I demonstrate an
application of the proposed algorithm to the complex domain of simulated
adaptive packet routing in a telecommunication network. I conclude by
suggesting how to build an intelligent agent and where to apply
reinforcement learning in computer vision and natural language processing.
Keywords: MDP, POMDP, policy search, gradient methods, reinforcement
learning, adaptive systems, stochastic control, adaptive behavior.
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