Title :
The need for improved reinforcement learning techniques in intelligent agents
Author_Institution :
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX, USA
Abstract :
Reinforcement learning is an integral part of intelligent agent research. The development of this field, however, has been largely independent of the latest developments in neural networks. As a result, the most popular designs for intelligent agents utilize neural network architectures from several years ago. This article recommends newer, proven designs for reinforcement learning. The recommended designs share historical roots with the most popular architectures in place today, allowing improved performance without radical redesign of existing agents
Keywords :
artificial intelligence; learning (artificial intelligence); neural nets; software agents; adaptive critics; intelligent agents; neural networks; reinforcement learning; software agents; Computational intelligence; Computer networks; Cost function; Dynamic programming; Equations; Intelligent agent; Laboratories; Machine learning; Neural networks; World Wide Web;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614403
