Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
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 -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.
Keywords
Cite
@article{arxiv.2304.03291,
title = {Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms},
author = {Ali Beikmohammadi and Sindri Magnússon},
journal= {arXiv preprint arXiv:2304.03291},
year = {2023}
}
Comments
Accepted in the 16th AGI Conference (AGI-23), Stockholm, Sweden, June 16 - June 19, 2023. arXiv admin note: text overlap with arXiv:2212.12517