期刊
NEURAL NETWORKS
卷 16, 期 1, 页码 5-9出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0893-6080(02)00228-9
关键词
reinforcement learning; dopamine; dynamic environment; meta-learning; meta-parameters; neuromodulation; TD error
Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner. We tested our algorithm in both a simulation of a Markov decision task and in a non-linear control task. Our results show that the algorithm robustly finds appropriate meta-parameter values, and controls the meta-parameter time course, in both static and dynamic environments. We suggest that the phasic and tonic components of dopamine neuron firing can encode the signal required for meta-learning of reinforcement learning. (C) 2002 Elsevier Science Ltd. All rights reserved.
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