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Reinaldo A. C. Bianchi Carlos H. C. Ribeiro Anna H. R. Costa 《Journal of Heuristics》2008,14(2):135-168
This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement
learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to
combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim,
we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning
algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning
algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant
performance enhancement of the learning rate. 相似文献
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G. Bianchi 《Il Nuovo Cimento C》1990,13(1):177-188
Summary Selenia Spazio was involved since its beginning, more than twenty years ago, in scientific space missions, like almost all
space industries in the world. In fact the European space organizations were mainly devoted to the development of autonomous
launchers and to scientific research in the nearby and in the far away space. First programs in which Selenia Spazio had a
role, designing and manufacturing space electronic equipment, were scientific satellites and ELDO launchers, followed by COSB
and other cosmic research missions. In all these years cosmic physics and related research programs continued to receive technological
support and see full involvement by space industry.
To speed up publication, the proofs were not sent to the authors and were supervised by the Scientific Committee. 相似文献
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