Agent Training Using Action Priorities for Complex Subtasks
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Solution Overview
Problem
Existing methods for training agents are inefficient and inaccurate in automating complex tasks across different applications, particularly in handling multiple subtasks and adapting to dynamic environments.
Innovation Solution
A method for training agents by determining action priorities based on experience data, selecting high-value target experience data, and training using a combination of dominance values, uncertainty penalties, instant and long-term rewards, and meta-learning to improve accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agent training methods are used to handle complex tasks across different applications, then the agent can perform basic functions, but the training efficiency and accuracy are insufficient
Solution Approach 1:
The patent segments the training process into multiple iterations, where each iteration focuses on selecting and processing specific experience data. The experience pool is divided into multiple experience sets that are processed sequentially, allowing the agent to gradually improve its policy through focused learning on different subsets of experiences.
Solution Approach 2:
The patent performs preliminary actions by pre-processing experience data before training. This includes determining action priorities, calculating dominance values, and selecting target experience data in advance. These preliminary steps prepare the data in an optimal format for training, improving both efficiency and accuracy.
2Productivity
If the agent processes all experience data equally, then comprehensive learning is achieved, but the training process becomes inefficient and time-consuming
Solution Approach 1:
The patent applies local quality by treating different experience data differently based on their value. Instead of uniform processing, the system calculates dominance values and action priorities for each experience set, selectively focusing training resources on high-value experiences while reducing attention to less important ones.
Solution Approach 2:
The patent changes parameters by introducing dominance values and action priorities as new dimensions for evaluating experience data. These parameter transformations allow the system to quantify the value of different experiences and adjust training focus dynamically, balancing speed and completeness.
3Adaptability or versatility
If the agent uses simple training methods, then the implementation is straightforward, but the agent cannot adapt to dynamic environments and complex subtasks
Solution Approach 1:
The patent introduces dynamics by making the training process adaptive rather than static. The system dynamically calculates dominance values and action priorities based on the current state of the experience pool, allowing the training focus to shift automatically as the agent learns. This dynamic adjustment enables adaptation to changing environments and task complexities.
Solution Approach 2:
The patent implements feedback mechanisms through the calculation of dominance values and action priorities. The system continuously evaluates the value of different experience sets based on their contribution to learning, using this feedback to select which experiences to process next. This feedback loop enables the agent to adapt its training strategy based on performance and environmental conditions.
Data Source
AI summary
A method for training an agent includes: for each subtask of a sample task, determining action priorities for a plurality of first candidate actions in a plurality of sets of experience data corresponding to the subtask in an experience pool of the agent, wherein the action priorities represent values of the plurality of the first candidate actions; selecting target experience data corresponding to the subtask from the plurality of sets of experience data based on the action priorities; and training the agent based on the target experience data.