Robust Memory Augmentation by Constrained Latent Imagination
The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way. While learning from imagined trajectories by the latent model is confirmed to has great potential to facilitate behavior learning, the lack of memory diversity limits generalization capability. Inspired by a neuroscience experiment of “forming artificial memories during sleep”, we propose a robust memory augmentation method with Constrained Latent ImaginatiON (CLION) under a novel actor-critic framework, which aims to speed up the learning of the optimal policy with virtual episodic. Various experiments on high-dimensional visual control tasks with arbitrary image uncertainty demonstrate that CLION outperforms existing approaches in terms of data-efficiency, robustness to uncertainty, and final performance.
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