Title :
The need for improved reinforcement learning techniques in intelligent agents
Author :
Wunsch, Donald ; Prokhorov, Danil
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. The 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 :
adaptive systems; dynamic programming; learning (artificial intelligence); neural nets; adaptive critic designs; critic net; dynamic programming; improved reinforcement learning techniques; intelligent agent research; neural network architectures; Computational intelligence; Computer networks; Cost function; Dynamic programming; Equations; Intelligent agent; Laboratories; Machine learning; Neural networks; Technological innovation;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.633059
