Self-Supervised Object-Level Deep Reinforcement Learning

3 Mar 2020William AgnewPedro Domingos

Current deep reinforcement learning approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency. We incorporate a few object-based priors that humans are known to use: "Infants divide perceptual arrays into units that move as connected wholes, that move separately from one another, that tend to maintain their size and shape over motion, and that tend to act upon each other only on contact" [Spelke]... (read more)

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