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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Real Time Robot Policy Adaptation Based on Intelligent Algorithms
verfasst von : Genci Capi, Hideki Toda, Shin-Ichiro Kaneko
Erschienen in: Artificial Intelligence Applications and Innovations
Verlag: Springer Berlin Heidelberg
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In this paper we present a new method for robot real time policy adaptation by combining learning and evolution. The robot adapts the policy as the environment conditions change. In our method, we apply evolutionary computation to find the optimal relation between reinforcement learning parameters and robot performance. The proposed algorithm is evaluated in the simulated environment of the Cyber Rodent (CR) robot, where the robot has to increase its energy level by capturing the active battery packs. The CR robot lives in two environments with different settings that replace each other four times. Results show that evolution can generate an optimal relation between the robot performance and exploration-exploitation of reinforcement learning, enabling the robot to adapt online its strategy as the environment conditions change.