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1.	Beikmohammadi, Ali, 1995-, et al. (author) Comparing NARS and Reinforcement Learning : An Analysis of ONA and Q-Learning Algorithms 2023 In: Artificial General Intelligence. - : Springer. - 9783031334696 - 9783031334689 ; , s. 21-31 Conference paper (peer-reviewed)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.

Beikmohammadi, Ali, 1995-, et al. (author)
Comparing NARS and Reinforcement Learning : An Analysis of ONA and Q-Learning Algorithms
2023
In: Artificial General Intelligence. - : Springer. - 9783031334696 - 9783031334689 ; , s. 21-31
Conference paper (peer-reviewed)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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