Dynamic Agent Grouping for Context-Specific Reinforcement Learning
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Solution Overview
Problem
Existing systems for interactive personal assistants lack the specificity of training agents for individual contexts, failing to leverage shared contexts between objects and often suffer from insufficient training data, leading to inefficient decision-making and recommendations.
Innovation Solution
Dynamic agent grouping and reinforcement learning methods are employed, where agents are clustered based on similarity in a parameter space, sharing information to train a shared agent model that provides context-sensitive feedback, balancing the efficiency of single-agent training with the specificity of individual contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents are trained individually for specific contexts, then the specificity and personalization of recommendations improve, but the amount of training data available to each agent becomes insufficient
Solution Approach 1:
The patent merges training data from multiple similar agents into a shared training pool. Agents that are determined to be similar based on parameter space clustering contribute their individual data to a common pool, which is then used to train all agents in the group. This combining approach allows each agent to benefit from the cumulative training data of the entire group rather than being limited to its own scarce data.
Solution Approach 2:
The patent introduces an intermediary mechanism (the agent manager and parameter space clustering system) that mediates between individual agents and the training data pool. This intermediary determines similarity between agents, manages the shared training pool, and coordinates the training process, enabling efficient data sharing while maintaining agent-specific personalization.
2Productivity
If a single shared agent model is used for all agents, then training efficiency improves, but the personalization and context-specific accuracy of recommendations deteriorates
Solution Approach 1:
The patent segments agents into different groups based on their similarity in parameter space. Rather than using a single universal model for all agents or training each agent completely independently, the system divides agents into clusters and trains each cluster with shared data from similar agents. This segmentation allows efficient data sharing within groups while maintaining specificity across different agent types.
Solution Approach 2:
The patent applies local quality by providing different training data compositions to different agent groups. Each agent receives training data that is locally optimized for its specific context and characteristics, drawn from the shared pool of similar agents. This ensures that each agent group gets training data that is most relevant to its specific operational context while still benefiting from the efficiency of shared training.
3Quantity of substance
If training data is shared between all agents, then the quantity of training data increases, but the relevance and applicability of shared data to individual agent contexts decreases
Solution Approach 1:
The patent ensures that each agent receives training data with locally appropriate quality and relevance. By using parameter space clustering to identify similar agents, the system ensures that data shared between agents comes from contexts that are sufficiently similar to be relevant. This prevents the dilution of data quality that would occur with universal sharing while still achieving increased data volume through selective sharing.
Data Source
AI summary
Methods and systems for reinforcement learning with dynamic agent grouping include gathering information at a first agent using one or more sensors. Shared information is received at the first agent from a second agent. An agent model is trained at the first agent using the gathered information and the shared information. A contribution of the shared information is weighted according to a degree of similarity between the first agent and the second agent. An action is generated using the trained agent model responsive to the gathered information.