期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 48, 期 1, 页码 29-40出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2618926
关键词
Actor-critic; adaptive self-organizing map (SOM); multiple-model; off-policy reinforcement learning (RL); optimal control
类别
资金
- NSF [ECCS-1405173, IIS-1208623]
- ONR [N00014-13-1-0562, N000141410718]
- Czech Ministry of Education, Youth and Sports [LO1506]
- Czech Science Foundation [GA 15-12068S]
- Fulbright Program
In this paper, motivated by human neurocognitive experiments, a model-free off-policy reinforcement learning algorithm is developed to solve the optimal tracking control of multiple-model linear discrete-time systems. First, an adaptive self-organizing map neural network is used to determine the system behavior from measured data and to assign a responsibility signal to each of system possible behaviors. A new model is added if a sudden change of system behavior is detected from the measured data and the behavior has not been previously detected. A value function is represented by partially weighted value functions. Then, the off-policy iteration algorithm is generalized to multiple-model learning to find a solution without any knowledge about the system dynamics or reference trajectory dynamics. The off-policy approach helps to increase data efficiency and speed of tuning since a stream of experiences obtained from executing a behavior policy is reused to update several value functions corresponding to different learning policies sequentially. Two numerical examples serve as a demonstration of the off-policy algorithm performance.
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