Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
This work addresses the problem of finding alternatives to reinforcement learning for researchers and practitioners in machine learning, but it is incremental as it compares existing methods without introducing new ones.
The paper compared the Non-Axiomatic Reasoning System (NARS) as an alternative to reinforcement learning for sequence-based tasks, finding that NARS showed competitive performance, especially in non-deterministic environments, using ONA and Q-Learning in OpenAI Gym setups.
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.