Estimating scale-invariant future in continuous time

18 Feb 2018Zoran TiganjSamuel J. GershmanPer B. SederbergMarc W. Howard

Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (model-based algorithms) or a scalar value of exponentially-discounted future reward using the Bellman equation (model-free algorithms)... (read more)

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