The famous problem here is the “noisy TV problem”. If your AI is driven to go towards regions of uncertainty then it will be completely captivated by a TV on the wall showing random images, no need for a copy of Doom, any random giberish that the AI can’t predict will work.
OpenAI claims to have already solved the noisy TV problem via Random Network Distillation, although I’m still skeptical of it. I think it’s a clever hack that only solves a specific subclass of this problem that is relatively superficial.
Well, one may develop an AI that handles noisy TV by learning that it can’t predict the noisy TV. The idea was to give it a space that is filled with novelty reward, but doesn’t lead to a performance payoff.
The famous problem here is the “noisy TV problem”. If your AI is driven to go towards regions of uncertainty then it will be completely captivated by a TV on the wall showing random images, no need for a copy of Doom, any random giberish that the AI can’t predict will work.
OpenAI claims to have already solved the noisy TV problem via Random Network Distillation, although I’m still skeptical of it. I think it’s a clever hack that only solves a specific subclass of this problem that is relatively superficial.
Well, one may develop an AI that handles noisy TV by learning that it can’t predict the noisy TV. The idea was to give it a space that is filled with novelty reward, but doesn’t lead to a performance payoff.