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：近年来，强化学习（Reinforcement learning, RL）已成为机器学习中解决序列任务的主流方法。然而，寻找RL的可行替代方案仍是一个令人振奋且富有创新性的研究领域。非公理推理系统（Non-Axiomatic Reasoning System, NARS）作为一种通用认知推理框架，已引起广泛关注。本文深入探讨了NARS作为RL替代方案在解决序列任务中的潜力。为此，我们在基于OpenAI Gym创建的不同难度环境（从简单到复杂）中，对NARS的实现ONA与$Q$-学习算法的性能进行了比较分析。结果表明，NARS是RL的一种有前景的替代方案，在多种环境（尤其是不确定性环境）中展现出竞争性表现。