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ROBOCUP
2000
Springer

Improvement Continuous Valued Q-learning and Its Application to Vision Guided Behavior Acquisition

10 years 2 months ago
Improvement Continuous Valued Q-learning and Its Application to Vision Guided Behavior Acquisition
Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to converge. This makes it difficult to be applied to real robot tasks because of poor performance of learned behavior and further a new problem of state space construction. We have proposed Continuous Valued Q-learning for real robot applications, which calculates contribution values to estimate a continuous action value in order to make motion smooth and effective [1]. This paper proposes an improvement of the previous work, which shows a better performance of desired behavior than the previous one, with roughly quantized state and action. To show the validity of the method, we applied the method to a vision-guided mobile robot of which task is to chase a ball.
Yasutake Takahashi, Masanori Takeda, Minoru Asada
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 2000
Where ROBOCUP
Authors Yasutake Takahashi, Masanori Takeda, Minoru Asada
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