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Parallelization by Vectorization in Fuzzy Rule Interpolation Adapted to FRIQ-Learning

Reinforcement learning algorithms can have a relatively high computational demand when working with more than a few state dimensions and possible actions. Especially when a complex algorithm is being used, as in the case of FRIQ-learning, which is a reinforcement learning method based on fuzzy rule interpolation (FRI). With the aim of reducing the computational resource needs, this paper presents possible optimization strategies for the FRIQ-learning reinforcement algorithm, including optimization of the underlying fuzzy rule interpolation method incorporated in FRIQ-learning. The goal was to identify parallelization possibilities and exploit fine-grained parallelism in FRIQ-learning. This was realized by rearranging data structures and with using the Advanced Vector Extensions 2 (AVX2) SIMD instruction set present in most modern x86 architecture processors. Experimental results are also presented through well-known reinforcement learning benchmark problems. The techniques described here can be also useful for optimizing other FRI methods for vectorization.