Neural Network Skill Library Expansion via Reinforcement Learning
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Solution Overview
Problem
Current reinforcement learning methods rely heavily on a comprehensive skill library pre-trained in a specific environment, which is resource-intensive and may not cover all tasks, leading to limitations in task completion.
Innovation Solution
A neural network obtaining method that incrementally learns skills by selecting mature neural networks trained in similar tasks and refining them using reinforcement learning algorithms, expanding the skill library and reducing environmental requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive skill library is pre-trained in a specific environment, then the skill coverage is improved, but the resource consumption and training complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks on similar tasks beforehand to build a reusable skill library. The server pre-trains multiple neural networks on different similar tasks, extracts their skills, and stores them in a skill library. When a new task arrives, the system can directly utilize these pre-trained skills rather than training from scratch, significantly reducing training complexity while maintaining comprehensive skill coverage.
Solution Approach 2:
The patent implements universality by creating a multi-functional skill library that can serve multiple different tasks. The system trains neural networks on various similar tasks and consolidates their skills into a universal skill library that can be applied to different task types. This allows the same skill library to handle diverse tasks, reducing the need for task-specific training and lowering overall training complexity.
2Adaptability or versatility
If a comprehensive skill library is pre-trained in a specific environment, then the skill coverage is improved, but the resource consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-training neural networks and extracting skills in advance. The server pre-trains neural networks on similar tasks, extracts skills, and stores them in a skill library before actual task execution. This preliminary skill extraction and storage reduces the computational resources needed during actual task processing, as the system can directly apply pre-extracted skills rather than performing full training procedures.
Solution Approach 2:
The patent applies copying by replicating successful skills from one neural network to another. The system extracts skills from pre-trained neural networks and copies them to new neural networks handling similar tasks. This copying mechanism allows the reuse of trained skills across multiple tasks without retraining, significantly reducing resource consumption while maintaining comprehensive skill coverage.
3Reliability
If the skill library includes many skills, then the task completion capability is improved, but the possibility of incomplete task coverage remains
Solution Approach 1:
The patent implements dynamics by making the skill library adaptive and continuously expandable. The system allows for dynamic addition of new skills to the skill library when tasks are not fully covered. When a neural network fails to complete a task or achieves below-threshold success rates, the system can introduce new neural networks with additional skills, dynamically expanding the skill library's coverage while maintaining high task completion capability for existing tasks.
Solution Approach 2:
The patent applies feedback by using task completion results to guide skill library expansion. The system monitors the success rates of neural networks in completing tasks and uses this feedback to determine when to add new skills. When tasks are not fully covered or success rates are below thresholds, the feedback mechanism triggers the addition of new neural networks and skills, ensuring continuous improvement of both task completion capability and coverage.
4Device complexity
If reinforcement learning is performed with a fixed skill library, then the training process is simplified, but the ability to learn new skills is reduced
Solution Approach 1:
The patent makes the skill library dynamic rather than fixed. The system allows the skill library to be expanded by adding new neural networks and skills during the reinforcement learning process. When tasks are not fully covered or performance thresholds are not met, the system dynamically introduces new skills, maintaining simple training processes for existing tasks while preserving the capability to learn and incorporate new skills as needed.
Data Source
AI summary
A neural network obtaining method and a related device are provided. The method may be applied to a scenario in which reinforcement learning is performed on a neural network in the artificial intelligence field. After obtaining a first task, a server obtains a first success rate of completing the first task by using a first neural network. When the first success rate is less than a preset threshold, the server generates a second neural network and a new skill. The server trains, based on a simulated environment corresponding to the first task, the second neural network by using a reinforcement learning algorithm, until a second success rate of completing the first task by using the second neural network is greater than or equal to the preset threshold. The server stores the trained second neural network and the new skill.