Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers

We introduced a working memory augmented adaptive controller in our recent work. The controller uses attention to read from and write to the working memory. Attention allows the controller to read specific information that is relevant and update its working memory with information based on its relevance, similar to how humans pick relevant information from the enormous amount of information that is received through various senses. The retrieved information is used to modify the final control input computed by the controller. We showed that this modification speeds up learning.In the above work, we used a soft-attention mechanism for the adaptive controller. Controllers that use soft attention update and read information from all memory locations at all the times, the extent of which is determined by their relevance. But, for the same reason, the information stored in the memory can be lost. In contrast, hard attention updates and reads from only one location at any point of time, which allows the memory to retain information stored in other locations. The downside is that the controller can fail to shift attention when the information in the current location becomes less relevant.We propose an attention mechanism that comprises of (i) a hard attention mechanism and additionally (ii) an attention reallocation mechanism. The attention reallocation enables the controller to reallocate attention to a different location when the relevance of the location it is reading from diminishes. The reallocation also ensures that the information stored in the memory before the shift in attention is retained which can be lost in both soft and hard attention mechanisms. Through detailed simulations of various scenarios for two link robot robot arm systems we illustrate the effectiveness of the proposed attention mechanism.