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> Methodologically, how do we create agents that aren't just good at several tasks, but make up their own tasks

It's a good question, it has been asked a few times, and there are some answers[1][2] already, with the most general being to endow the agent with intrinsic motivation defined as an information-theoretic objective to maximize some definition of surprise. Then the agent in question will develop a general curious exploration policy, if trained long enough.

> Further, how do we create agents that can learn without ever seriously failing?

Another good question. One of the good enough answers here is that you should design a sequence of value functions[3] for your agent, in such a way, as to enforce some invariants over its future, possibly open-ended, lifetime. For this specific concern you should ensure that your agent develops some approximation of fear, leading to aversion of catastrophic failure regions in its state space. It's pretty self-evident that we develop such a fear in the young age ourselves, and where we don't, evolution gives us a hand and makes us preemptively fear heights, or snakes, even before we ever see one.

The other answer being, of course, to prove[4] a mathematical theorem around some hard definition of safe exploration in reinforcement learning.

1. https://people.idsia.ch/~juergen/interest.html

2. https://www.deepmind.com/publications/is-curiosity-all-you-n...

3. https://www.frontiersin.org/articles/10.3389/fncom.2016.0009...

4. https://arxiv.org/abs/2006.03357




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