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Correntropy kernel temporal differences for reinforcement learning brain machine interfaces

This paper introduces a novel temporal difference algorithm to estimate a value function in reinforcement learning. This is a kernel adaptive system using a robust cost function called correntropy. We call this system correntropy kernel temporal differences (CKTD). This algorithm is integrated with Q-learning to find a proper policy (Q-learning via correntropy kernel temporal differences). The proposed method was tested with a synthetic problem, and its robustness under a changing policy was quantified. The same algorithm was applied to the decoding of a monkey's neural states in a reinforcement learning brain machine interface (RLBMI) in a center-out reaching task. The results showed the potential advantage of the proposed algorithm in the RLBMI framework.