论文标题
NARS与强化学习：ONA与Q学习
NARS vs. Reinforcement learning: ONA vs. Q-Learning
论文作者
论文摘要
现实的场景之一是采取一系列最佳动作来执行任务。强化学习是处理机器学习社区中这种任务的最著名方法。找到合适的替代方案总是很有趣且开箱即用的问题。因此，在这个项目中，我们希望调查NARS的能力，并回答NARS是否有可能替代RL的问题。特别是，我们在开放的AI健身房开发的某些环境中进行了比较。实验的源代码在以下链接中公开可用：\ url {https://github.com/alibeikmohammadi/opennars-for-applications/tree/master/master/misc/misc/python}。
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: \url{https://github.com/AliBeikmohammadi/OpenNARS-for-Applications/tree/master/misc/Python}.
