|
|||||||||||
Reinforcement Learning Algorithm for
Solving RTDP with Variational Environment College of Computer and Communicational Engineering,
Changsha University of
Science and Technology, 45 Chiling Road, Changsha,
Hunan, 410076, China Abstract: Reinforcement learning algorithm based-on solving RTDP does not have to evaluate the entire state space in order to deliver an optimal policy, and yet it can often obtain good policy. The DP always is employed to be a learning Agent's environment. In this paper, we propose a novel algorithm called V-RTDP to solve Agent learning problems with full observability. Here, we use a new DP framework in varying environment instead of a general one. Our experiment demonstrates that the algorithm is very efficient. Keywords: Real-time Dynamic Programming, Variational Environment, Reinforcement Learning, Algorithm, Optimal Policy (
Biographies:
BibTex: @ARTICLE{P1120715001, AUTHOR = {Changming Yin and Lihua Xue and Minghui Hu and Liyun Li}, TITLE = {Reinforcement Learning Algorithm for Solving RTDP with Variational Environment}, JOURNAL = {ICGST International Journal on Artificial Intelligence and Machine Learning, AIML},
YEAR = {2007},
VOLUME = {7}, ISSUE ={1}, PAGES={17--21} } ( |
|||||||||||
|