Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers
We introduced a {\it 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. 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 or hard attention mechanisms are limited either because they can forget the information or fail to shift attention when the information they are reading 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. We illustrate through detailed simulations of various scenarios for two link robot and three link robot arm systems we illustrate the effectiveness of the proposed attention mechanism.
Code (0)
등록된 구현이 없습니다.
Tasks
Hard AttentionSimilar Papers 제목 키워드 기반
Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains
Stochastic optimal control methods often struggle in complex non-convex landscapes, frequently becoming trapped in local optima due to their inability to learn from historical trajectory data. This paper introduces Memor…
Computational EfficiencyPartially Non-Recurrent Controllers for Memory-Augmented Neural Networks
Memory-Augmented Neural Networks (MANNs) are a class of neural networks equipped with an external memory, and are reported to be effective for tasks requiring a large long-term memory and its selective use. The core modu…
Dual Control Memory Augmented Neural Networks for Treatment Recommendations
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history …
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory
Significant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. While these approaches, which combine high-level…
Instruction FollowingMinecraftAgentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM…
Reinforcement Learning