  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 states, the explorer plans to discover unseen
surprising states through foresight, which are then used as diverse targets for
the achiever to practice. After the unsupervised phase, LEXA solves tasks
specified as goal images zero-shot without any additional learning. LEXA
substantially outperforms previous approaches to unsupervised goal-reaching,
both on prior benchmarks and on a new challenging benchmark with a total of 40
test tasks spanning across four standard robotic manipulation and locomotion
domains. LEXA further achieves goals that require interacting with multiple
objects in sequence. Finally, to demonstrate the scalability and generality of
LEXA, we train a single general agent across four distinct environments. Code
and videos at https://orybkin.github.io/lexa/
