Autor: |
Xiang MU, Quan LIU, Qi-ming FU, Hong-kun SUN, Xin ZHOU |
Jazyk: |
čínština |
Rok vydání: |
2013 |
Předmět: |
|
Zdroj: |
Tongxin xuebao, Vol 34, Pp 92-99 (2013) |
Druh dokumentu: |
article |
ISSN: |
1000-436X |
DOI: |
10.3969/j.issn.1000-436x.2013.10.011 |
Popis: |
When dealing with the continuous space problems,the traditional Q-iteration algorithms based on lookup-table or function approximation converge slowly and are diff lt to get a continuous policy.To overcome the above weak-nesses,an on-policy TD algorithm named DFP-OPTD was proposed based on double-layer fuzzy partitioning and its convergence was proved.The first layer of fuzzy partitioning was applied for state space,the second layer of fuzzy parti-tioning was applied for action space,and Q-value functions were computed by the combination of the two layer fuzzy partitioning.Based on the Q-value function,the consequent parameters of fuzzy rules were updated by gradient descent method.Applying DFP-OPTD on two classical reinforcement learning problems,experimental results show that the algo-rithm not only can be used to get a continuous action policy,but also has a better convergence performance. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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