Hypermodel Environment Models for Uncertainty-Guided 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 affects the agent's ability to effectively explore and perform tasks.
Innovation Solution
A system utilizing a hypermodel that processes index variables from a continuous space to specify parameters of 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 probabilistic model that represents a continuous ensemble of models through a probability distribution over model parameters. This allows the system to capture uncertainty across infinitely many environment models while performing computations efficiently through sampling from the distribution, rather than training and maintaining separate finite ensemble models.
Solution Approach 2:
The probabilistic model serves multiple functions simultaneously: it represents environmental dynamics, quantifies uncertainty about the environment, enables efficient computation through sampling, and provides a framework for both exploration and exploitation. This multi-functional approach replaces the need for separate finite ensemble models that could only represent uncertainty in a limited way.
2Measurement precision
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 changes the parameter representation from discrete model parameters in finite ensembles to continuous probability distributions over model parameters. This allows the system to represent uncertainty more richly by capturing the full spectrum of possible environmental configurations, while managing complexity through efficient sampling and evaluation techniques rather than explicitly maintaining all possible models.
Solution Approach 2:
The system transitions from representing uncertainty through a finite number of discrete models to representing uncertainty through a continuous probability distribution across an infinite space of models. This dimensional transformation from discrete to continuous space enables superior uncertainty representation while computational techniques keep the system manageable.
3Productivity
If separate training of environment models is performed, then each model can be optimized, but the overall system performance and exploration efficiency deteriorates
Solution Approach 1:
The system uses feedback from environmental interactions to update the probability distribution over model parameters, allowing the ensemble to adapt and improve its representation of environmental uncertainty over time. This feedback mechanism enables coordinated optimization across all models in the continuous ensemble, improving both exploration efficiency and task performance compared to separate training approaches.
Solution Approach 2:
The system performs preliminary sampling from the probability distribution to generate multiple environment model realizations before actual task execution. This allows the agent to plan and explore considering multiple possible environmental configurations simultaneously, improving exploration efficiency and leading to more robust task performance across uncertain environments.
Data Source
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.