23,300 research outputs found
Solving Factored MDPs with Hybrid State and Action Variables
Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions. The central idea of HALP is to approximate the optimal value
function by a linear combination of basis functions and optimize its weights by
linear programming. We analyze both theoretical and computational aspects of
this approach, and demonstrate its scale-up potential on several hybrid
optimization problems
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Feature Reinforcement Learning: Part I: Unstructured MDPs
General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the MDP framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into one learning algorithm.
Extensions to more realistic dynamic Bayesian networks are developed in Part
II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram
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