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A comparative study of policies in Q-learning for foraging tasks

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3 Author(s)
Mohan, Y. ; Sch. of Eng., Monash Univ., Petaling Jaya, Malaysia ; Ponnambalam, S.G. ; Inayat-Hussain, J.I.

Q-learning is a machine learning technique that learns what to do and how to map states to actions to maximize rewards. Q-learning has been applied to various tasks such as foraging, soccer and prey-pursuing robots. In this paper, a simple foraging task has been considered to study the influences of the policies reported in the open literatures. A mobile robot is used to search and retrieve pucks back to a home location. The goal of this study is to identify an efficient policy for q-learning which maximizes the number of pucks collected and minimizes the number of collisions in the environment. Policies namely greedy, epsilon-greedy, Boltzmann distribution and random search are used to study their performances in the foraging task and the results are presented.

Published in:

Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on

Date of Conference:

9-11 Dec. 2009

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