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Discovering and Achieving Goals via World Models
[article]
2021
arXiv
pre-print
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discovering new goals and learning to reliably achieve them. We introduce Latent Explorer Achiever (LEXA), a unified solution to these that learns a world model from image inputs and uses it to train an explorer and an achiever policy from imagined rollouts. Unlike prior methods that explore by reaching previously visited
arXiv:2110.09514v1
fatcat:tdyd2jkixjaqzebetyi5qakeoa