In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.
翻译:Translated Abstract:
近年来,强化学习(RL)已成为机器学习中解决序列任务的流行方法。然而,寻找适当的替代方案仍是一个令人兴奋和创新的研究领域。其中一种引起关注的替代方案是非公理推理系统(NARS),它是一个通用的认知推理框架。在本文中,我们深入探讨了NARS作为RL替代方案在解决序列任务方面的潜力。为了调查这一点,我们在Open AI gym中创建了各种环境,使用ONA作为NARS实现和$Q$-Learning算法,进行了性能比较分析。这些环境具有不同的难度级别,从简单到复杂不等。我们的结果表明,NARS是RL的一个有希望的替代方案,在不同环境中具有竞争性的性能,特别是在非确定性环境中。