1990 IntegratedArchitectureforLearni

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Subject Headings: Reinforcement Learning.

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Abstract

This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We how that Dyna-Q architectures are easy to adapt for use in changing.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1990 IntegratedArchitectureforLearniRichard S. SuttonIntegrated Architecture for Learning, Planning, and Reacting based on Approximating Dynamic Programming1990
AuthorRichard S. Sutton +
titleIntegrated Architecture for Learning, Planning, and Reacting based on Approximating Dynamic Programming +
year1990 +