An empirical analysis of reinforcement learning using design of experiments
نویسندگان
چکیده
This study uses a design of experiments approach to understand the behavior of a neural network to learn the mountain car domain using the TD(λ) algorithm. A large experiment is first performed to characterize the probability of empirical convergence based on three parameters of the TD(λ) algorithm (λ, γ, ), and a logistic regression model is fitted to this data. A detailed analysis of the parameter subspace finds that, upon convergence, these parameters significant affect convergence speed and mean performance, though performance differences are minimal.
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