On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning
نویسندگان
چکیده
In this theoretical contribution, we provide mathematical proof that two of the most important classes of network learning-correlation-based differential Hebbian learning and reward-based temporal difference learning-are asymptotically equivalent when timing the learning with a modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation-based perspective more closely related to the biophysics of neurons.
منابع مشابه
On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor
In this theoretical contribution we provide mathematical proof that two of the most important classes of network learning correlation-based differential Hebbian learning and reward-based temporal difference learning are asymptotically equivalent when timing the learning with a local modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement lear...
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ورودعنوان ژورنال:
- Neural computation
دوره 21 4 شماره
صفحات -
تاریخ انتشار 2009