Online Fitted Reinforcement Learning
Workshop Paper, ICML '95 Value Function Approximation in Reinforcement Learning Workshop, July, 1995
Abstract
My paper in the main portion of the conference deals with fitted value iteration or Q-learning for offline problems, {em i.e.}, those where we have a model of the environment so that we can examine arbitrary transitions in arbitrary order. The same techniques also allow us to do Q-learning for an online problem, {em i.e.}, one where we have no model but must instead perform experiments inside the MDP to gather data. I will describe how.
BibTeX
@workshop{Gordon-1995-16193,author = {Geoffrey Gordon},
title = {Online Fitted Reinforcement Learning},
booktitle = {Proceedings of ICML '95 Value Function Approximation in Reinforcement Learning Workshop},
year = {1995},
month = {July},
}
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