Relative Value Function Approximation TITLE2:

Authors:
Paul E. Utgoff;Doina Precup
Affiliations:
-;-
Venue:
Relative Value Function Approximation TITLE2:
Year:
1997

Citing 0
Cited 2

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

Machine Learning
Automatic induction of bellman-error features for probabilistic planning

Journal of Artificial Intelligence Research

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Abstract

A form of temporal difference learning is presented that learns the relative utility of states, instead of the absolute utility. This formulation backs up decisions instead of values, making it possible to learn a simpler function for defining a decision-making policy. A nonlinear relative value function can be learned without increasing the dimensionality of the inputs.