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arXiv · 2304.03291

Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms

Ali Beikmohammadi·Sindri Magnússon
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Abstract

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.

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Ali Beikmohammadi, Sindri Magnússon. 2023-03-17. Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms. https://arxiv.org/abs/2304.03291

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