# NARS vs. Reinforcement learning: ONA vs. Q-Learning

arXiv: https://arxiv.org/abs/2212.12517 · HTML: https://mlsanity.com/paper/2212.12517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 23 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](https://mlsanity.com/paper/1802.06501/) (2018)
- [Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic](https://mlsanity.com/paper/2112.10504/) (2021)
- [SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN](https://mlsanity.com/paper/2009.05139/) (2020)

## Built on by (most cited first, 1 of 1)

- [TabQL: In-Context Q-Learning with Tabular Foundation Models](https://mlsanity.com/paper/2605.18979/) (2026)

## Abstract (arXiv)

One of the realistic scenarios is taking a sequence of optimal actions to do a task. Reinforcement learning is the most well-known approach to deal with this kind of task in the machine learning community. Finding a suitable alternative could always be an interesting and out-of-the-box matter. Therefore, in this project, we are looking to investigate the capability of NARS and answer the question of whether NARS has the potential to be a substitute for RL or not. Particularly, we are making a comparison between Q-Learning and ONA on some environments developed by an Open AI gym. The source code for the experiments is publicly available in the following link: urlhttps://github.com/AliBeikmohammadi/OpenNARS-for-Applications/tree/master/misc/Python.