To the left, you see a trained agent playing a level of a game. To the right, you see the same playthrough from an agent-centric perspective: cropped, translated, and rotated with the agent in the center. Which perspective is the best input for the agent? https://arxiv.org/abs/2001.09908 pic.twitter.com/7bCtBp8xUG
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This paper builds on earlier work, where we showed that standard deep RL algorithms learn policies that generalize very badly. They are barely able to play any levels that they were not trained on at all.https://twitter.com/togelius/status/1012726654261702658 …
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It is well known from "non-deep" agent learning research (say, evolutionary robotics work and game AI work from early to mid 2000s) that the sensor representation is extremely important to the agent's ability to learn. http://julian.togelius.com/VanHoorn2009Hierarchical.pdf …pic.twitter.com/Ugj9IDsdDR
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The promise/premise of deep learning is that we don't have to worry about these representations, because the network will figure out the input representation itself. But, really, will it? Will a network of a few layers really learn to rotate and translate to focus on the agent?
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Also, can it? It is hard to imagine that a neural network of just a few layers could actually implement the transformations necessary to even understand where things are relative to the agent, so that the policy can be location-independent?
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It is possible that the standard paradigm of a neural network with a handful of layers learning to master, say, Atari games from a static third-person view is actually impossible. That is, it doesn't learn any general playing skills. It learns some kind of stimulus-response table
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In any case, even if this is possible in principle, it seems that the way we represent the input makes a lot of difference for the generality of skills that can be learned in practice.
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