Off-policy Control Policy Evaluation via Virtual Replica
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Solution Overview
Problem
Existing methods for evaluating neural network-based control policies require real-world interaction, which can be unsafe, especially in applications like robotics, and do not allow for effective off-policy evaluation without actual deployment, limiting model selection and optimization to simulation or controlled environments.
Innovation Solution
A system that evaluates a control policy's performance using offline data from a source agent and environment, allowing for off-policy evaluation without deploying the policy in the target environment, by processing observation-action pairs with a trained Q neural network to determine a performance estimate for deployment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world interaction is used to evaluate control policies, then evaluation accuracy is improved, but safety deteriorates due to potential harm to robots or environment
Solution Approach 1:
The patent creates a virtual copy of the target environment and agent, allowing the control policy to be evaluated in this simulated replica rather than the real physical system. The virtual environment replicates the dynamics and characteristics of the target system, enabling accurate off-policy evaluation without exposing the real robot to harmful actions from poorly performing policies.
2Reliability
If real-world deployment is required for policy evaluation, then evaluation reliability is improved, but cost and time increase due to costly test deployments
Solution Approach 1:
The patent performs preliminary evaluation of control policies in a virtual environment before any real-world deployment. By pre-evaluating multiple candidate policies offline using collected trajectory data, the system identifies promising policies that can then be deployed with confidence, eliminating the need for time-consuming and costly iterative real-world testing.
3Object-affected harmful factors
If offline data from source agent is used for off-policy evaluation, then safety is improved by avoiding real-world interaction, but measurement precision deteriorates without actual deployment
Solution Approach 1:
The patent introduces a virtual environment as an intermediary between the offline data and the evaluation process. This virtual replica serves as a mediator that allows accurate evaluation of policies using only offline trajectory data, bridging the gap between safety constraints and evaluation precision requirements without needing direct real-world interaction.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for off-policy evaluation of a control policy. One of the methods includes obtaining policy data specifying a control policy for controlling a source agent interacting with a source environment to perform a particular task; obtaining a validation data set generated from interactions of a target agent in a target environment; determining a performance estimate that represents an estimate of a performance of the control policy in controlling the target agent to perform the particular task in the target environment; and determining, based on the performance estimate, whether to deploy the control policy for controlling the target agent to perform the particular task in the target environment.