Method and apparatus for pruning deep neural network based Q-learning empirical memory

摘要

The present technology discards or adds new experiences based on collecting new experiences by the agent, comparing the new experiences with the experiences stored in the memory of the agent, and the comparison. And using the experience to overwrite the in-memory experience. For example, the agent or associated processor can determine how similar the new experience is to the stored experience. If the new experience is overly similar, the agent discards the new experience, otherwise the agent stores the new experience in memory and discards the previously stored experience instead. Do. Collecting experiences and selectively storing these experiences based on the similarity of these experiences to previously stored experiences addresses technical issues and results in multiple technical improvements. For example, memory size constraints are reduced, opportunities for catastrophic forgetting by neural networks are reduced or eliminated, and neural network performance is improved. [Selected figure] Figure 7