Hypermodel-Based Environment Modeling for Efficient Agent Exploration
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Solution Overview
Problem
Existing machine learning systems for controlling agents in environments face inefficiencies and limitations in representing uncertainty, particularly when training multiple environment models separately, which hampers effective exploration and task performance.
Innovation Solution
A system utilizing a hypermodel that processes index variables from a continuous space to generate parameter values for an environment model, enabling joint training of infinitely many environment models, thereby improving computational efficiency and uncertainty representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple environment models are trained separately in a finite ensemble, then each model can be optimized independently, but the computational efficiency decreases and the uncertainty representation becomes limited
Solution Approach 1:
The patent merges multiple environment models into a unified hierarchical structure where a hypermodel generates parameters for multiple environment models. This allows the system to represent uncertainty across infinitely many environment models while training them jointly through shared experience buffers and centralized optimization, thereby improving both uncertainty representation and computational efficiency compared to separate training of finite ensembles.
Solution Approach 2:
The hypermodel serves multiple functions simultaneously: it generates parameters for multiple environment models, transforms probability distributions over continuous index variables into parameter distributions, and enables joint training of infinitely many models through a single unified architecture. This multi-functionality resolves the contradiction by providing comprehensive uncertainty representation without the computational overhead of separate model training.
2Reliability
If a finite ensemble of environment models is used, then the system complexity remains manageable, but the ability to represent environmental uncertainty is insufficient
Solution Approach 1:
The patent implements a nested hierarchical structure where a hypermodel contains and generates parameters for multiple environment models, which in turn generate observations for the agent. This nesting allows the system to represent infinitely many environment models within a manageable architectural framework, improving uncertainty representation while controlling system complexity through the hierarchical organization.
Solution Approach 2:
The hypermodel acts as an intermediary that transforms the probability distribution over continuous index variables into parameter distributions for environment models. This intermediary layer enables the system to represent uncertainty across infinitely many models without directly managing the complexity of each individual model, thereby improving reliability while maintaining manageable system complexity.
3Productivity
If infinitely many environment models are jointly trained, then computational efficiency improves and uncertainty representation enhances, but the training process becomes more complex
Solution Approach 1:
The patent merges the training processes of infinitely many environment models into a single joint training framework. By sharing experience buffers, parameters, and optimization objectives across all models through the hypermodel, the system achieves computational efficiency improvements while managing training complexity through unified architecture and centralized optimization procedures.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes sampling one or more index variables from a continuous space of possible index variables in accordance with a probability distribution over the continuous space; for each index variable: processing the index variable using a hypermodel, in accordance with values of a plurality of parameters of the hypermodel, to generate an output that specifies values of a plurality of parameters of an environment model; and generating an action selection output using the environment model in accordance with the values of the plurality of parameters of the environment model that are specified by the hypermodel output for the index variable; and selecting the action to be performed by the agent at the time step using the one or more action selection outputs for the one or more index variables.