Cloud ML Heuristic with Decision Look-up Table for Mobile Devices
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Solution Overview
Problem
Existing machine learning algorithms, such as Q-Learning, face challenges with slow learning rates, high memory usage, and network dependency, particularly in distributed systems, which can lead to unresponsive devices and increased costs due to constant communication requirements.
Innovation Solution
A Cloud-based ML heuristic that uses a Decision Look-up Table (DLUT) on client devices for decision-making, reducing network usage and dependency, with the server maintaining a full Q-Table for updates, allowing for asynchronous operations and faster training times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Q-Learning is implemented on client devices with full Q-Table, then decision-making capability is improved, but memory usage increases and network dependency increases
Solution Approach 1:
The patent extracts the Q-Table from client devices and places it on the server. Client devices only store a simplified decision look-up table (DLUT) that maps states to actions, while the full Q-Table is maintained on the server for centralized updates and training. This extraction significantly reduces memory requirements on client devices while maintaining decision-making capability.
Solution Approach 2:
The server acts as an intermediary between client devices and the full Q-Learning system. It receives result data from clients, updates the Q-Table, and generates updated DLUTs for client devices. This intermediary approach allows clients to operate independently with minimal memory while still benefiting from centralized learning.
2Measurement precision
If constant communication with server is maintained for Q-Learning updates, then learning accuracy is improved, but network latency increases and system responsiveness decreases
Solution Approach 1:
The server pre-calculates and generates decision look-up tables (DLUTs) based on the Q-Table before client devices need them. Client devices can operate asynchronously using these pre-generated DLUTs without requiring constant communication or real-time updates, significantly reducing network latency and improving responsiveness.
Solution Approach 2:
The system implements an asynchronous feedback mechanism where client devices send result data to the server periodically rather than continuously. The server uses this feedback to update the Q-Table and regenerate DLUTs, maintaining learning accuracy while allowing client devices to operate independently with minimal network communication.
3Productivity
If distributed Q-Learning with constant communication is used, then training effectiveness is improved, but device complexity and network costs increase
Solution Approach 1:
The patent extracts the complex Q-Table management from client devices and centralizes it on the server. Client devices only handle simple state-to-action lookups using DLUTs, dramatically reducing device complexity. The server handles all complex Q-Learning operations, training effectiveness, and coordinate updates.
Solution Approach 2:
Instead of each client maintaining a full copy of the Q-Table, the system generates simplified DLUT copies from the server's Q-Table for client devices. These DLUTs are much smaller and sufficient for local decision-making, reducing both device complexity and network bandwidth requirements while maintaining training effectiveness through centralized updates.
Data Source
AI summary
A method for a first electronic device comprises generating a decision-making data structure using a machine learning data structure; transmitting, to a second electronic device, the decision-making data structure; receiving, from the electronic device, result data regarding a result of performing a selected action selected from the decision-making data structure; and updating the machine learning data structure using the result data.