Reinforcement-learning agents with different temperature parameters explain the variety of human action-selection behavior in a Markov decision process task - researchr publication

researchr

You are not signed in
Sign in
Sign up

Fumihiko Ishida, Takahiro Sasaki, Yutaka Sakaguchi, Hiroyuki Shimai. Reinforcement-learning agents with different temperature parameters explain the variety of human action-selection behavior in a Markov decision process task. Neurocomputing, 72(7-9):1979-1984, 2009. [doi]

Abstract is missing.

runs on WebDSL