TY - GEN
T1 - Stochastic learning and optimization - A sensitivity-based approach
AU - Cao, Xi Ren
PY - 2008
Y1 - 2008
N2 - We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic systems. We show that this sensitivity-based view provides a unified framework for many different disciplines in this area, including perturbation analysis, Markov decision processes, reinforcement learning, and identification and adaptive control. Many results can be simply derived and intuitively explained by using two performance sensitivity formulas. In addition, we show that this sensitivity-based view opens up new directions for future research. For example, the event-based optimization, which has advantages over the state-based approaches, may be developed with this sensitivity-based view.
AB - We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic systems. We show that this sensitivity-based view provides a unified framework for many different disciplines in this area, including perturbation analysis, Markov decision processes, reinforcement learning, and identification and adaptive control. Many results can be simply derived and intuitively explained by using two performance sensitivity formulas. In addition, we show that this sensitivity-based view opens up new directions for future research. For example, the event-based optimization, which has advantages over the state-based approaches, may be developed with this sensitivity-based view.
UR - https://www.scopus.com/pages/publications/79961019207
U2 - 10.3182/20080706-5-KR-1001.4270
DO - 10.3182/20080706-5-KR-1001.4270
M3 - Conference Paper published in a book
AN - SCOPUS:79961019207
SN - 9783902661005
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
BT - Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
T2 - 17th World Congress, International Federation of Automatic Control, IFAC
Y2 - 6 July 2008 through 11 July 2008
ER -