AI-Vision-Robotics Division Colloquium
and
Seminar on Computational Learning and Adaptation
Remembrance of Things Past
Leslie
Pack Kaelbling
Computer Science Department
Brown University
Providence, RI 02912
lpk@cs.brown.edu
Behaving effectively in partially observable environments requires remembering
something about the past. Partially bservable Markov decision processes
(POMDPs) provide a formal model for describing and evaluating control problems
that require memory. An agent in an unknown POMDP has two main strategies:
learn a model of the POMDP, then solve for a good policy or learn a policy
directly.
I will begin by describing an algorithm for learning POMDP models for
robot navigation, which, coupled with previous work on controlling POMDPs,
yields a behavior learning system. Then, I'll talk about some very
recent work on direct approaches to learning policies for POMDPs without
first learning a model. I'll conclude with a description of a new
project on learning models for visual navigation in humans and robots.
| Date: Thurs., March 4 |
Time: 4:15pm
|
Place: Gates 104
|
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