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

Autor:	Beikmohammadi, Ali, Magnússon, Sindri
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in the 16th AGI Conference (AGI-23), Stockholm, Sweden, June 16 - June 19, 2023. arXiv admin note: text overlap with arXiv:2212.12517
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.03291 Zobrazit plný text záznamu View this record from Arxiv