Decentralized Multi-Agent RL Control for Communication-Limited Vehicles
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Solution Overview
Problem
Existing systems for controlling unmanned vehicles lack complete autonomous control capabilities, particularly in communication-compromised scenarios, leading to operational inefficiencies and increased risks in both commercial and civilian operations.
Innovation Solution
A decentralized multi-agent actor-critic reinforcement learning model is employed, utilizing multi-modal neural networks to process image and parameter data, enabling each vehicle to make independent decisions based on localized and centralized information, with a critic model providing utility feedback to refine actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators remotely control unmanned vehicles, then precise human oversight is achieved, but operational continuity is compromised when communication is disrupted
Solution Approach 1:
The control system is segmented into multiple independent neural network agents, each capable of autonomous decision-making for specific vehicles. This segmentation allows the system to maintain operational continuity through decentralized intelligence while reducing dependency on centralized human control during communication disruptions.
Solution Approach 2:
The unmanned vehicles are equipped with autonomous neural network agents that enable self-service capability. These agents can independently process sensor data, make decisions, and execute actions without continuous human intervention, ensuring operational continuity when communication with human operators is lost.
2Extent of automation
If multiple neural network agents are deployed for each vehicle, then autonomous decision-making capability is improved, but computational complexity increases
Solution Approach 1:
The complex control problem is segmented into multiple specialized neural network agents, each handling specific aspects of vehicle control. This segmentation distributes computational complexity across multiple modular components rather than requiring a single monolithic system, making the overall system more manageable and scalable.
Solution Approach 2:
The neural network agents are designed with multi-functionality to handle various control tasks using unified architectural patterns. This universality reduces overall system complexity by reusing proven computational frameworks across multiple agents rather than developing specialized systems for each function.
Data Source
AI summary
A computerized system configured to execute a multi-agent machine learning model for controlling a plurality of vehicles in a multi-vehicle autonomous control session in a multi-vehicle environment is disclosed. Multi-modal neural network agents of the model each control a corresponding autonomous vehicle in the session. The agents receive image data and parameter data, input the image data to an image feature extractor to produce an image feature vector, input the parameter data to a parameter data feature extractor to produce a parameter data feature vector, produce a joint latent representation of the image data and parameter data, and input the joint latent representation to an actor model neural network, to generate a selected action for the autonomous vehicle. The multi-agent machine learning model is configured to control each autonomous vehicle in the session according to the corresponding selected action for each autonomous vehicle.