NARS vs. Reinforcement learning: ONA vs. Q-Learning
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
10 of 34 references.
References · 10
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2015Trust Region Policy Optimizationcited by 2,580
- 2016OpenAI Gymcited by 1,736
- 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
- 2019Reinforcement Learning in Healthcare: A Surveycited by 310
- 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
- 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
- 2021Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Criticcited by 23
- 2020SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNNcited by 4
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026TabQL: In-Context Q-Learning with Tabular Foundation Models18 May 2026