Graph Neural Network Policy for Reinforcement Learning Efficiency
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Solution Overview
Problem
Reinforcement learning methods face low training efficiency due to the large number of parameters in deep neural networks, especially when data or training rounds are limited, leading to suboptimal policy function gains.
Innovation Solution
The use of a graph neural network as the policy function, combined with a structure graph obtained through learning, to improve training efficiency by understanding environmental structure and guiding intelligent agent interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deep neural network is used as the policy function, then the model can handle complex environments, but the training efficiency becomes low due to the large number of parameters
Solution Approach 1:
The patent segments the policy function into multiple specialized neural networks, each responsible for specific actions or environmental aspects. This segmentation reduces the parameter count of each individual network while maintaining overall model capability through coordinated action of multiple specialized networks
Solution Approach 2:
The patent introduces a new dimension by incorporating structure graphs that represent environmental topology and relationships. This graphical structure dimension complements the traditional neural network approach, enabling the system to leverage structural knowledge and reduce reliance on purely data-driven parameter learning
2Measurement precision
If a large number of parameters are used in the policy function, then the model can capture complex patterns, but the expected gain is low when data or training rounds are limited
Solution Approach 1:
The patent performs preliminary action by pre-processing environmental information into structure graphs that encode spatial relationships, connectivity, and topological features. This preliminary structuring of data allows the neural networks to learn more efficiently from fewer training rounds, as the structural context is already organized and available
Solution Approach 2:
The patent changes the parameter representation by using structure graphs with nodes and edges that encode environmental features. This transforms the parameter space from high-dimensional raw sensor data to a more compact, structurally-informed representation that requires fewer training parameters and less training time
3Adaptability or versatility
If traditional reinforcement learning is used without structure information, then the agent can operate in any environment, but the time required to find better solutions is extended
Solution Approach 1:
The patent introduces structure graphs as an intermediary representation between the environment and the neural network policy functions. These graphs serve as mediators that encode environmental structure and relationships, enabling faster learning by providing the agent with structured knowledge about the environment rather than requiring it to discover all patterns through trial and error
Data Source
AI summary
A reinforcement learning method and recognition apparatus includes: obtaining a structure graph, where the structure graph includes structure information that is of an environment or the intelligent agent and that is obtained through learning; inputing a current state of the environment and the structure graph to a policy function of the intelligent agent, where the policy function is used to generate an action in response to the current state and the structure graph, and the policy function of the intelligent agent is a graph neural network; outputing the action to the environment by using the intelligent agent; obtaining, from the environment by using the intelligent agent, a next state and reward data in response to the action; training the intelligent agent through reinforcement learning based on the reward data.