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The present paper proposes a novel reinforcement learning method with worldmodels, DreamingV2, a collaborative extension of DreamerV2 and Dreaming.DreamerV2 is a cutting-edge model-based reinforcement learning from pixels thatuses discrete world models to represent latent states with categoricalvariables. Dreaming is also a form of reinforcement learning from pixels thatattempts to avoid the autoencoding process in general world model training byinvolving a reconstruction-free contrastive learning objective. The proposedDreamingV2 is a novel approach of adopting both the discrete representation ofDreamingV2 and the reconstruction-free objective of Dreaming. Compared toDreamerV2 and other recent model-based methods without reconstruction,DreamingV2 achieves the best scores on five simulated challenging 3D robot armtasks. We believe that DreamingV2 will be a reliable solution for robotlearning since its discrete representation is suitable to describediscontinuous environments, and the reconstruction-free fashion well managescomplex vision observations.

DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction