论文标题
阿基米德陷阱：为什么传统的加强学习可能不会产生AGI
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI
论文作者
论文摘要
在以使其适应非数字结构的方式的方式概括了实数的Archimedean属性之后，我们证明了实数不能用于准确测量非架构的结构。我们认为，由于具有人工通用情报（AGI）的代理商应该没有问题，而从事固有涉及非Archimedean Rewards的任务，并且由于传统的强化学习奖励是实数，因此传统的强化学习可能不会导致AGI。我们指出，可以改变传统的增强学习方式以消除这种障碍。
After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock.
