Simulation of rat behavior by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals.
Brown and Wanger [Brown, R.T., Wanger, A.R., 1964. Resistance to punishment and extinction following training with shock or nonreinforcement. J. Exp. Psychol. 68, 503-507] investigated rat behaviors with the following features: (1) rats were exposed to reward and punishment at the same time, (2) environment changed and rats relearned, and (3) rats were stochastically exposed to reward and punishment. The results are that exposure to nonreinforcement produces resistance to the decremental effects of behavior after stochastic reward schedule and that exposure to both punishment and reinforcement produces resistance to the decremental effects of behavior after stochastic punishment schedule. This paper aims to simulate the rat behaviors by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals. The former algorithms of reinforcement learning were unable to simulate the behavior of the feature (3). We improve the former reinforcement learning algorithms by controlling learning parameters in consideration of the acquisition probabilities of reinforcement signals. The proposed algorithm qualitatively simulates the result of the animal experiment of Brown and Wanger.
Murakoshi
Kazushi
K
Department of Knowledge-based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi 441-8580, Japan. mura@tutkie.tut.ac.jp
Noguchi
Takuya
T
D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal / Q000502:physiology*; D005108:Extinction, Psychological / Q000502:physiology*; D008954:Models, Biological; D008960:Models, Psychological; D015233:Models, Statistical; D051381:Rats; D012054:Reinforcement, Psychology