Reward Estimation Model for Delayed-Feedback Resource Allocation
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Solution Overview
Problem
Existing computer models, particularly reinforcement learning systems, face challenges in making accurate predictions due to delayed feedback, leading to inefficient resource allocation when reward information is not immediately available.
Innovation Solution
A combined machine learning model integrating learning using privileged information (LUPI) and multi-armed bandits (MAB) paradigms to enhance reward estimation, leveraging internal data to estimate delayed metrics and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems are used to make predictions with delayed feedback, then flexibility and adaptability are improved, but measurement precision of reward estimation deteriorates
Solution Approach 1:
The patent introduces an intermediary reward estimation model that bridges the gap between delayed feedback and real-time decision-making. This model predicts intermediate reward values based on available features and privileged information, allowing the RL system to operate with accurate reward estimates even when actual feedback is delayed. The intermediary model acts as a mediator that translates delayed outcomes into immediate guidance for policy optimization.
Solution Approach 2:
The system performs preliminary actions by training a reward estimation model in advance using privileged information and historical data. This pre-trained model is then deployed to generate real-time reward estimates during RL training, enabling the system to act as if it has immediate feedback. The preliminary preparation of the estimation model allows the RL agent to make informed decisions without waiting for actual delayed rewards.
2Measurement precision
If privileged information is used during training, then measurement precision of reward estimation is improved, but device complexity increases
Solution Approach 1:
The patent segments the overall system into distinct components: a privileged information processing module, a reward estimation model, and an RL training module. Each component has a specific function and processes specific types of data. The privileged information module handles only the additional informative features, while the reward estimation model focuses on predicting reward values, and the RL module handles policy optimization. This segmentation reduces complexity by distributing functionality across specialized sub-systems.
Solution Approach 2:
The reward estimation model serves multiple functions: it estimates rewards for RL training, provides guidance for policy optimization, and can potentially be used for other predictive tasks. By creating a multi-functional component, the patent reduces overall system complexity rather than adding separate specialized modules for each function. The same model structure handles both the estimation task and the integration with RL training.
Data Source
AI summary
A method includes applying a training controller to an untrained predictive model and training data to generate a trained predictive model. The training data includes privileged information. Applying the training controller is an iterative process that repeats until convergence and includes a loss function determination phase, an update phase that updates the untrained predictive model, and a test phase that includes applying the training data to an updated version of the untrained predictive model. The privileged information is applied during the loss function determination phase and excluded during the test phase. The method also includes integrating the trained predictive model into a reward estimation function to generate a trained combined model. The trained predictive model's output includes an input to the reward estimation function. The trained predictive model's output includes a reward determination for the reward estimation function. The trained combined model is presented.