Research of Reinforcement Learning Control of Intelligent Robot Based on Fuzzy-CMAC Network

Pan, Lian; Tong, Yaobin

doi:10.1109/cnmt.2009.5374686

Cited by 1 publication

(1 citation statement)

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“…These results suggest that learning an approximation of the value function online is essential to the effective combination of function approximation techniques with Q-Learning. Research has shown that Q-Learning can be effectively combined with function approximation techniques (Ritthipravat et al 2004;Pan and bin Tong 2009) to learn a robot control policy, with continuous sensor measurements (states) and actuator demands (actions), using a reward signal. Incorporating function approximation techniques does usually forfeit the guarantees of convergence but, in reality, practical precautions can be used to minimise divergence.…”

Section: Learning a Robot Controllermentioning

confidence: 99%

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Waldock

Carse

2015

Soft Comput

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The majority of machine learning techniques applied to learning a robot controller generalise over either a uniform or pre-defined representation that is selected by a human designer. The approach taken in this paper is to reduce the reliance on the human designer by adapting the representation to improve the generalisation during the learning process. An extension of a Hierarchical Fuzzy Rule-Based System (HFRBS) is proposed that identifies and refines inaccurate regions of a fuzzy controller, while interacting with the environment, for both supervised and reinforcement learning problems. The paper shows that a controller using an adaptive HFRBS can learn a suitable control policy using a fewer number of fuzzy rules for both a supervised and reinforcement learning problem and is not sensitive to the layout as with a uniform representation. In supervised learning problems, a small number of extra trials are required to find an effective representation but for reinforcement learning problems, the process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy and hence open the door to high-dimensional problems.

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Section: Learning a Robot Controllermentioning

confidence: 99%