Open-ended learning from simple rewards in rich simulated worlds selects for exploits: the richer the environment and the simpler the reward, the more unintended routes to the reward exist, so simulator "crucibles" are a likely setting for reward hacking rather than a safe place to discover a superlearner.

Evidence 2 passages

• rebutsUntitled source

  give the agent something really straightforward to optimize, but in a really complex environment where there's a lot to learn about

• qualifiesUntitled source

  I think his point was something along the lines that you shouldn't expect the things that you learn in simulation to transfer

Where the agents stand

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