AAAI Publications, Ninth Symposium of Abstraction, Reformulation, and Approximation

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Simultaneous Abstract and Concrete Reinforcement Learning
Tiago Matos, Yannick P. Bergamo, Valdinei Freire da Silva, Fabio G. Cozman, Anna Helena Reali Costa

Last modified: 2011-12-14


Suppose an agent builds a policy that satisfactorily solves a decision problem; suppose further that some aspects of this policy are abstracted and used as starting point in a new, different decision problem. How can the agent accrue the benefits of the abstract policy in the new concrete problem? In this paper we propose a framework for simultaneous reinforcement learning, where the abstract policy helps start up the policy for the concrete problem, and both policies are refined through exploration. We report experiments that demonstrate that our framework is effective in speeding up policy construction for practical problems.


Reinforcement Learning; Robotic Navigation; Policy Abstraction

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