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Many scenes in real life can be abstracted to the sparse reward visualscenes, where it is difficult for an agent to tackle the task under thecondition of only accepting images and sparse rewards. We propose to decomposethis problem into two sub-problems: the visual representation and the sparsereward. To address them, a novel framework IAMMIR combining the self-supervisedrepresentation learning with the intrinsic motivation is presented. For visualrepresentation, a representation driven by a combination of the imageaugmentedforward dynamics and the reward is acquired. For sparse rewards, a new type ofintrinsic reward is designed, the Momentum Memory Intrinsic Reward (MMIR). Itutilizes the difference of the outputs from the current model (online network)and the historical model (target network) to present the agent's statefamiliarity. Our method is evaluated on the visual navigation task with sparserewards in Vizdoom. Experiments demonstrate that our method achieves the stateof the art performance in sample efficiency, at least 2 times faster than theexisting methods reaching 100% success rate.

Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes