Decentralized Nodal Graph Reinforcement Learning for Moving Agents
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Solution Overview
Problem
Conventional goods transport techniques rely on centralized decision-making, leading to inefficiencies and limited ability to address delays, shortages, and demand/supply spikes due to a single point of failure and lack of decision-making speed.
Innovation Solution
A decentralized decision-making process using a nodal graph generated by moving agents, which includes data on themselves, requests, and goods, fed into a reinforcement learning-based graphical neural network to determine actions for satisfying requests, allowing autonomous and connected vehicles to operate efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized supply chain infrastructure is used for decision-making, then coordination and control are simplified, but the system has a single point of failure and cannot adequately address demand and supply spikes
Solution Approach 1:
The patent divides the centralized decision-making system into multiple decentralized decision-making units (moving agents), each capable of autonomous decision-making. This segmentation eliminates the single point of failure while distributing the decision-making complexity across multiple independent units that can operate autonomously.
Solution Approach 2:
The patent implements dynamic decision-making where each moving agent can independently adapt its behavior based on real-time conditions. This dynamic approach allows the system to respond flexibly to demand and supply spikes without requiring complex centralized coordination, as each agent autonomously adjusts its decisions based on local information.
2Speed
If centralized planning entity makes all decisions, then system-wide coordination is achieved, but decision-making speed is insufficient to address sudden supply and demand changes
Solution Approach 1:
Each moving agent is equipped with the capability to make its own decisions autonomously based on local information, eliminating the need for slow centralized processing. This self-service approach enables rapid decision-making at the source while preserving local information that would otherwise be lost in centralized processing.
Solution Approach 2:
The patent pre-equips each moving agent with decision-making algorithms and capabilities, allowing them to immediately respond to changing conditions without waiting for centralized instructions. This preliminary preparation of decision-making capacity at each agent enables rapid response to supply and demand changes while maintaining coordination through standardized communication protocols.
3Productivity
If decentralized decision-making is implemented, then system reliability and responsiveness improve, but coordination complexity increases
Solution Approach 1:
The patent implements a universal communication protocol and standardized data format that all moving agents use for interaction. This universality enables decentralized decision-making while simplifying coordination, as all agents can exchange information and cooperate using the same standardized interface, reducing the complexity that would otherwise arise from heterogeneous communication methods.
Data Source
AI summary
A method for managing moving agents is provided. The method comprises identifying goods agents, moving agents, and a plurality of requests within a predetermined area, generating a nodal graph including the moving agents, requests, and goods as vertices and edges defining relations between two vertices, obtaining actions for the moving agents by inputting the nodal graph to a reinforcement-learning based graphical neural network model stored in the moving agents, the reinforcement-learning based graphical neural network outputs the action for the moving agent in response to receiving the nodal graph, and instructing the moving agents to operate based on the actions to satisfy at least one of the plurality of requests.