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

Beikmohammadi, Ali,1995-Stockholms universitet,Institutionen för data- och systemvetenskap (författare)

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

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.

Magnússon, Sindri,1987-Stockholms universitet,Institutionen för data- och systemvetenskap(Swepub:su)sima1283 (författare)
Stockholms universitetInstitutionen för data- och systemvetenskap (creator_code:org_t)

Ingår i:Artificial General Intelligence: Springer, s. 21-3197830313346969783031334689

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