Generative Model Guidance Data for Agent Trajectories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models struggle to provide effective guidance data to agents interacting with complex environments, as they lack the ability to leverage diverse strategies and interactions from multiple agents.
Innovation Solution
A system that generates guidance data by using a generative model conditioned on prompts based on target agent trajectories and a collection of reference trajectories from diverse agents, allowing for the comparison and contrast of strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning models are used to generate guidance data, then the system structure is simple, but the guidance data lacks effectiveness and informativeness
Solution Approach 1:
The patent combines multiple machine learning models (first ML model for trajectory generation, second ML model for feature extraction, third ML model for guidance generation) into an integrated system. This merging of models allows the system to leverage diverse strategies from reference agents while generating effective guidance data, resolving the contradiction between guidance effectiveness and system complexity.
Solution Approach 2:
The patent introduces an intermediary prompt generation mechanism that translates reference agent trajectories into prompts for the generative model. This intermediary layer enables the system to effectively utilize reference trajectories without directly complexifying the core guidance generation process, thus improving guidance effectiveness while managing system complexity.
2Loss of information
If diverse reference trajectories from multiple agents are incorporated, then the guidance data becomes more informative, but the data processing complexity increases
Solution Approach 1:
The patent extracts key features from diverse reference trajectories using a dedicated feature extraction model. This extraction process identifies and isolates the most informative elements from the complex data, reducing information loss while managing processing complexity by focusing computation on essential features rather than raw trajectories.
Solution Approach 2:
The patent segments the data processing pipeline into distinct stages: trajectory generation, feature extraction, prompt generation, and guidance generation. Each stage processes specific portions of the data independently, reducing overall processing complexity while preserving information through coordinated processing across segments.
3Reliability
If the system processes and compares multiple reference trajectories, then the guidance quality improves, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by processing only the most relevant reference trajectories through the full pipeline. The system generates guidance data conditioned on selected prompts derived from reference trajectories, rather than processing all possible trajectories, thus improving guidance quality while reducing computational resource consumption.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating guidance data to be provided to a target agent interacting with an environment. In one aspect, a method comprises: generating a prompt to be provided to a generative model based at least in part on: (i) one or more target trajectories representing interactions of the target agent with the environment, and (ii) a plurality of reference trajectories representing interactions of each of a plurality of reference agents with the environment, wherein each of the plurality of reference agents differ from the target agent; and generating the guidance data for the target agent using the generative model while the generative model is conditioned on the prompt; and providing the guidance data to the target agent.