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Enhanced EVA with Threshold Limit of Similarity

In recent years, there has been a growing demand for efficient learning algorithms to apply reinforcing learning to real-world tasks. The algorithm, called EVA, utilized non-parametric Q-values without using neural networks to improve efficiency of learning. We pointed out the problems with EVA, and proposed the improved algorithm with even better performance. We conducted comparative experiments on several tasks to assess the efficacy of our proposed methodology.