Robards, Matthew; Sunehag, Peter; Sanner, Scott; Marthi, Bhaskara
We introduce the first online kernelized version of SARSA(?) to permit sparsifi- cation for arbitrary ? for 0 = ? = 1; this is possible via a novel kernelization of the eligibility trace that is maintained separately from the kernelized value function. This separation is crucial for preserving the functional structure of the eligibility trace when using sparse kernel projection techniques that are essential for memory efficiency and capacity control. The result is a simple and practical...[Show more]
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