Abstract
This paper focuses on the integral reinforcement learning (I-RL) for input-affine continuous-time (CT) nonlinear systems where a known time-varying signal called an exploration is injected through the control input. First, we propose a modified I-RL method which effectively eliminates the effects of the explorations on the algorithm. Next, based on the result, an actor-critic I-RL technique is presented for the same nonlinear systems with completely unknown dynamics. Finally, the least-squares implementation method with the exact parameterizations is presented for each proposed one which can be solved under the given persistently exciting (PE) conditions. A simulation example is given to verify the effectiveness of the proposed methods.
| Original language | English |
|---|---|
| Title of host publication | 2012 International Joint Conference on Neural Networks, IJCNN 2012 |
| DOIs | |
| Publication status | Published - 2012 Aug 22 |
| Event | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia Duration: 2012 Jun 10 → 2012 Jun 15 |
Publication series
| Name | Proceedings of the International Joint Conference on Neural Networks |
|---|
Other
| Other | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 |
|---|---|
| Country | Australia |
| City | Brisbane, QLD |
| Period | 12/6/10 → 12/6/15 |
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All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
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Integral reinforcement learning with explorations for continuous-time nonlinear systems. / Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho.
2012 International Joint Conference on Neural Networks, IJCNN 2012. 2012. 6252508 (Proceedings of the International Joint Conference on Neural Networks).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Integral reinforcement learning with explorations for continuous-time nonlinear systems
AU - Lee, Jae Young
AU - Park, Jin Bae
AU - Choi, Yoon Ho
PY - 2012/8/22
Y1 - 2012/8/22
N2 - This paper focuses on the integral reinforcement learning (I-RL) for input-affine continuous-time (CT) nonlinear systems where a known time-varying signal called an exploration is injected through the control input. First, we propose a modified I-RL method which effectively eliminates the effects of the explorations on the algorithm. Next, based on the result, an actor-critic I-RL technique is presented for the same nonlinear systems with completely unknown dynamics. Finally, the least-squares implementation method with the exact parameterizations is presented for each proposed one which can be solved under the given persistently exciting (PE) conditions. A simulation example is given to verify the effectiveness of the proposed methods.
AB - This paper focuses on the integral reinforcement learning (I-RL) for input-affine continuous-time (CT) nonlinear systems where a known time-varying signal called an exploration is injected through the control input. First, we propose a modified I-RL method which effectively eliminates the effects of the explorations on the algorithm. Next, based on the result, an actor-critic I-RL technique is presented for the same nonlinear systems with completely unknown dynamics. Finally, the least-squares implementation method with the exact parameterizations is presented for each proposed one which can be solved under the given persistently exciting (PE) conditions. A simulation example is given to verify the effectiveness of the proposed methods.
UR - http://www.scopus.com/inward/record.url?scp=84865092901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865092901&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252508
DO - 10.1109/IJCNN.2012.6252508
M3 - Conference contribution
AN - SCOPUS:84865092901
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
ER -