Meta-Agent Selection for Diverse Reinforcement Learning Tasks

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Solution Overview

Problem

Existing reinforcement learning systems face challenges in efficiently selecting the most appropriate agent from a plurality of agents to solve a given problem, lacking a systematic approach to balance exploration and exploitation.

Innovation Solution

A meta-agent is introduced to select the optimal reinforcement learning agent based on predefined value types, enabling dynamic selection of agents based on the specific context and available training data, balancing complexity and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple reinforcement learning agents are used to solve a problem, then the system can handle diverse tasks and environments, but the complexity of selecting the appropriate agent increases

Engineering Contradiction:

Improveability to handle diverse tasksVSAvoidagent selection complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a meta-agent as an intermediary component that receives the current state and available actions, evaluates them using multiple reinforcement learning agents, and selects the optimal action. This mediator architecture allows the system to leverage multiple specialized agents without requiring complex selection logic at the application level, as the meta-agent handles the coordination and decision-making process centrally.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the problem-solving function into multiple specialized reinforcement learning agents, each potentially optimized for specific types of tasks or environments. The meta-agent then segments the decision-making process into state evaluation, action generation, and selection phases. This segmentation allows each component to focus on specific aspects, reducing overall system complexity while maintaining versatility.

Inventive Principle:

Principle #1Segmentation

2Productivity

If a meta-agent is introduced to select the optimal agent, then the efficiency of reinforcement learning is enhanced, but the device complexity increases

Engineering Contradiction:

Improvereinforcement learning efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The meta-agent is designed as a universal component that can work with multiple different reinforcement learning agents and handle various types of tasks and environments. It provides multi-functional capabilities by evaluating states and generating actions applicable across diverse scenarios, reducing the need for task-specific selection logic and improving overall system efficiency despite the added architectural layer.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12387138B2Meta-agent for reinforcement learning

Publication Date: 2025.08.12 BRAZE INC

AI summary

A computing machine receives a request for an action by a meta-agent at the computing machine. The computing machine selects, using the meta-agent, an agent for determining an action responsive to the request. The agent is selected from a plurality of reinforcement learning agents. The agent is selected based on values in a dataset associated with the request. At least a portion of the dataset has predefined value types. The meta-agent and each agent from the plurality of reinforcement learning agents is trained using datasets including the predefined value types. The computing machine transmits, to the selected agent, the request for the action to cause the agent to compute the action.