Tatsuji Takahashi

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Some of the authors have previously proposed a cognitively inspired reinforcement learning architecture (LS-Q) that mimics cognitive biases in humans. LS-Q adaptively learns under uniform, coarse-grained state division and performs well without parameter tuning in a giant-swing robot task. However, these results were shown only in simulations. In this(More)
In an uncertain environment, decision-making meets two opposing demands. One is to explore new information, while the other is to exploit already acquired information. The opposition is long called the exploration-exploitation dilemma. In brain science, it is known that human brain estimates options comparatively, and the average behavior correlates to the(More)
Many algorithms and methods in artificial intelligence or machine learning were inspired by human cognition. As a mechanism to handle the exploration-exploitation dilemma in reinforcement learning, the loosely symmetric (LS) value function that models causal intuition of humans was proposed (Shinohara et al., 2007). While LS shows the highest correlation(More)