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

arXiv: https://arxiv.org/abs/2304.03291 · HTML: https://mlsanity.com/paper/2304.03291/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)

## Built on by (most cited first, 3 of 3)

- [SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2308.02594/) (2023)
- [Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis](https://mlsanity.com/paper/2503.17454/) (2025)
- [NALA: an Effective and Interpretable Entity Alignment Method](https://mlsanity.com/paper/2404.11968/) (2024)

## Abstract (arXiv)

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.