Obfuscated Environment Model for Privacy-Preserving RL Training
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Solution Overview
Problem
Existing machine learning models face challenges in training action selection systems for complex environments while maintaining privacy and efficiency, as they require direct interaction with the target environment, which can expose sensitive information and consume significant computational resources.
Innovation Solution
A system that modifies the target environment model to create an obfuscated environment model, allowing users to interact through a simulation, generating user-generated training data that is then mapped back to the target environment, using reinforcement learning techniques to train the action selection system, thus maintaining privacy and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct interaction with target environment is used for training, then training data quality is improved, but privacy protection deteriorates
Solution Approach 1:
The patent creates a simulated environment that copies the essential dynamics and structure of the target environment without exposing sensitive information. Users interact with this simulated copy instead of the real environment, generating training data that reflects target environment behavior while maintaining privacy protection through the abstraction layer.
2Reliability
If direct interaction with target environment is used for training, then training effectiveness is improved, but computational resource consumption deteriorates
Solution Approach 1:
The patent introduces a simulated environment as an intermediary between users and the target environment. This intermediary generates training data through user interactions without requiring direct access to or extensive computational resources for the actual target environment, thereby reducing computational overhead while maintaining training effectiveness.
3Loss of information
If obfuscated environment model is used, then privacy protection is improved, but user interaction complexity deteriorates
Solution Approach 1:
The patent modifies parameters of the environment model by introducing controlled obfuscation elements such as hidden states, modified transition probabilities, and masked observations. These parameter changes protect sensitive information while maintaining sufficient environmental dynamics for effective training data generation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection system used to select actions to be performed by an agent interacting with a target environment to perform a task in the target environment. In one aspect, a method comprises: obtaining a target environment model of the target environment; modifying the target environment model of the target environment to generate an obfuscated environment model of an obfuscated environment that represents an obfuscation of the target environment; obtaining, from each of a plurality of users, one or more obfuscated environment trajectories that represent interaction of the user with the obfuscated environment through the corresponding obfuscated environment simulation; mapping each of the obfuscated environment trajectories to a corresponding target environment trajectory; and training the action selection system on the target environment trajectories.