An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models
Bridging Supervised Learning and Reinforcement Learning for Enhanced Predictive Models
Traditional supervised learning (SL) assumes data points are independently and identically distributed (i.i.d.), which overlooks dependencies in real-world data. Reinforcement learning (RL), in contrast, models dependencies through state transitions. This paper introduces a novel framework that reformulates SL problems as RL tasks, enabling the application of Temporal Difference (TD) learning to a wider range of SL scenarios, especially where data exhibits inherent correlations.
Unlocking Enhanced Supervised Learning with Reinforcement Learning Paradigms
This study aims to bridge supervised learning (SL) and reinforcement learning (RL) by reformulating SL problems as RL tasks. This enables the application of sophisticated RL techniques to a wider array of SL scenarios, particularly where data exhibits inherent dependencies rather than independent and identically distributed (i.i.d.) properties. Our approach introduces novel temporal difference (TD) algorithms capable of accommodating diverse data types, establishing theoretical conditions for TD to outperform Ordinary Least Squares (OLS) in correlated noise environments, providing robust convergence guarantees, and validating these benefits empirically across various synthetic and real-world datasets.
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Average Performance Gain (Correlated Data)
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Theoretical Convergence Guarantee
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Diverse Data Types & Tasks Supported
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Real-World Datasets Validated
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This paper introduces a novel approach by reformulating traditional Supervised Learning (SL) problems as Reinforcement Learning (RL) tasks, specifically as Markov Reward Processes (MRP). This enables the application of Temporal Difference (TD) learning algorithms to a broader range of SL scenarios. The generalized TD learning model is designed to handle diverse data types, analogous to inverse link functions in Generalized Linear Models (GLMs), making it adaptable for tasks like regression and classification.
A key theoretical contribution is the analysis of conditions under which TD solutions outperform Ordinary Least Squares (OLS) solutions, particularly when data noise is correlated. The framework establishes that the generalized TD algorithm's update rule acts as a contractive generalized Bellman operator, ensuring convergence. This provides robust theoretical guarantees for both expected and sample-based TD updates, demonstrating its efficiency and effectiveness under specific data properties.
Empirical studies validate the theoretical findings using both synthetic and real-world datasets across linear and deep neural network settings. Results show that TD learning consistently outperforms baselines when data noise is positively correlated and remains competitive on standard SL tasks. The algorithm demonstrates robustness to hyperparameter choices and applicability across various tasks, from regression to image classification, confirming its practical utility and generalization capabilities.
89.10%
MNIST Fashion Accuracy (TD-Classify)
99.06%
MNIST Accuracy (TD-Classify)
Enterprise Process Flow
Traditional SL Problem
→
Data as MRP
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State Value = Label
→
Reward from Bellman Eq.
→
Generalized TD Learning
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Optimal Parameters
| TD Learning (with MRP) | OLS (i.i.d. Assumption) |
|---|---|
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Air Quality Prediction with TD-Reg
On the air quality dataset, which involves predicting CO concentration, our TD-Reg algorithm demonstrated superior performance. When using a transition matrix that favors transitions between closer points, TD-Reg achieved the best results. This suggests that real-world time-series data often exhibits inherent correlations, making the MRP framework and TD learning particularly effective. Traditional SGD (Reg) struggled, highlighting the benefits of the TD approach in handling structured dependencies.
3.384
House Price Prediction (TD-Reg RMSE)
23.763
Execution Time Prediction (TD-Reg RMSE)
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