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Planning has been very successful for control tasks with known environmentdynamics. To leverage planning in unknown environments, the agent needs tolearn the dynamics from interactions with the world. However, learning dynamicsmodels that are accurate enough for planning has been a long-standingchallenge, especially in image-based domains. We propose the Deep PlanningNetwork (PlaNet), a purely model-based agent that learns the environmentdynamics from images and chooses actions through fast online planning in latentspace. To achieve high performance, the dynamics model must accurately predictthe rewards ahead for multiple time steps. We approach this using a latentdynamics model with both deterministic and stochastic transition components.Moreover, we propose a multi-step variational inference objective that we namelatent overshooting. Using only pixel observations, our agent solves continuouscontrol tasks with contact dynamics, partial observability, and sparse rewards,which exceed the difficulty of tasks that were previously solved by planningwith learned models. PlaNet uses substantially fewer episodes and reaches finalperformance close to and sometimes higher than strong model-free algorithms.

Learning Latent Dynamics for Planning from Pixels