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Combining Self-organizing Maps with Mixtures of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia
[chapter]
2006
Lecture Notes in Computer Science
In a reward-seeking task performed in a continuous environment, our previous work compared several Actor-Critic (AC) architectures implementing dopamine-like reinforcement learning mechanisms in the rat's basal ganglia. The task complexity imposes the coordination of several AC submodules, each module being an expert trained in a particular subset of the task. We showed that the classical method where the choice of the expert to train at a given time depends on each expert's performance
doi:10.1007/11840541_33
fatcat:5urdqlvwsjhofhpfcltu4cgraq