Multi-Agent Recommendation for Global Charging Station Optimization
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Solution Overview
Problem
Existing intelligent recommendation systems for multiple agent objects can only achieve local optimization, failing to address the global optimization target.
Innovation Solution
A multi-agent reinforcement learning approach is employed to determine object execution actions of multiple agent objects based on a pre-trained strategy model, selecting a target object execution action that maximizes global benefits, thereby recommending the optimal object to achieve global optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing intelligent recommendation systems are used for multiple agent objects, then local optimization can be achieved, but global optimization target cannot be achieved
Solution Approach 1:
The system segments the recommendation problem into multiple independent agent objects, where each agent (charging station) independently determines its execution actions based on local observations and the multi-agent strategy model. This segmentation enables the system to handle complex multi-agent scenarios while achieving global optimization through coordinated independent decisions.
Solution Approach 2:
The patent transitions from traditional single-dimension recommendation to multi-dimensional joint decision-making by considering multiple agent objects simultaneously. The multi-agent strategy model operates in a higher-dimensional space where it can evaluate and coordinate actions across multiple agents, achieving global optimization that transcends local limitations.
2Loss of time
If traditional recommendation methods are used, then system complexity remains low, but long-term charging waiting times cannot be minimized
Solution Approach 1:
The system performs preliminary action by pre-training the multi-agent strategy model offline before actual recommendation operations. The model learns optimal strategies through simulated interactions and stores accumulated knowledge in its parameters. During runtime, the system only needs to query the pre-trained model, achieving long-term time optimization without adding real-time computational complexity.
Solution Approach 2:
Each agent object (charging station) serves itself by independently determining its execution actions based on the multi-agent strategy model and local observations. This self-service mechanism eliminates the need for a centralized controller, reducing system complexity while enabling coordinated global optimization through decentralized autonomous decision-making.
Data Source
AI summary
Provided are an intelligent recommendation method and apparatus, a model training method and apparatus, an electronic device, and a storage medium, which relate to artificial intelligence technologies, and are applicable to the intelligent recommendation and the intelligent transportation technologies. The intelligent recommendation method includes: determining an object recommendation request; determining, according to a multi-agent strategy model and the object recommendation request, object execution actions of at least two agent objects matching the object recommendation request; determining a target object execution action according to the object execution actions; and recommending the object recommendation request to a target agent object corresponding to the target object execution action.