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

Understand

Many scenes in real life can be abstracted to the sparse reward visual scenes, where it is difficult for an agent to tackle the task under the condition of only accepting images and sparse rewards.

• We propose to decompose this problem into two sub-problems: the visual representation and the sparse reward.
• To address them, a novel framework IAMMIR combining the self-supervised representation learning with the intrinsic motivation is presented.
• For visual representation, a representation driven by a combination of the imageaugmented forward dynamics and the reward is acquired.