Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
Volume 46, Issue 2, Pages 111-124Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.robot.2003.11.006
Keywords
reinforcement learning; normalized Gaussian network; evolutionary state recruitment strategy; peg pushing
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In recent robotics fields, much attention has been focused on utilizing reinforcement learning (RL) for designing robot controllers, since environments where the robots will be situated in should be unpredictable for human designers in advance. However there exist some difficulties. One of them is well known as 'curse of dimensionality problem'. Thus, in order to adopt RL for complicated systems, not only 'adaptability' but also 'computational efficiencies' should be taken into account. The paper proposes an adaptive state recruitment strategy for NGnet-based actor-critic RL. The strategy enables the learning system to rearrange/divide its state space gradually according to the task complexity and the progress of learning. Some simulation results and real robot implementations show the validity of the method. (C) 2003 Elsevier B.V. All rights reserved.
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