Reinforcement Learning Training Apparatus for Combinatorial Optimization
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Solution Overview
Problem
Current methods for training reinforcement learning models in combinatorial optimization problems face challenges in effectively determining optimal starting points and calculating evaluation values, leading to suboptimal performance in solving complex optimization tasks.
Innovation Solution
The proposed solution involves an apparatus with a starting point determinator, multi-explorer, trajectory evaluator, baseline calculator, advantage calculator, and parameter updater, which determine and update starting points and calculate evaluation values using reinforcement learning models to improve the training efficiency of reinforcement learning models in combinatorial optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning models are used to solve combinatorial optimization problems, then learning capability is improved, but training efficiency deteriorates due to difficulty in determining optimal starting points and calculating evaluation values
Solution Approach 1:
The training process is segmented into multiple parallel exploration trajectories, each starting from different starting points. The determination of starting points and calculation of evaluation values are separated into distinct modules (starting point determinator and trajectory evaluator), allowing independent optimization and parallel processing to improve training efficiency while maintaining learning capability
Solution Approach 2:
Starting points are determined in advance using the starting point determinator before the actual reinforcement learning exploration begins. This preliminary determination of starting points and evaluation criteria enables more efficient training by avoiding repeated optimization of these parameters during the learning process
2Measurement precision
If multiple starting points are used for exploration, then solution quality is improved, but computational complexity increases
Solution Approach 1:
The exploration process is divided into multiple independent trajectories, each starting from a different starting point determined by the starting point determinator. This segmentation allows parallel computation of multiple exploration paths without requiring complex coordination, improving solution quality while managing computational complexity through modular architecture
Solution Approach 2:
The trajectory evaluator and baseline calculator are designed to universally evaluate any exploration trajectory regardless of its starting point. This multi-functional evaluation system handles multiple starting points through a unified framework, reducing the need for separate evaluation mechanisms for each trajectory and thereby controlling computational complexity
Data Source
AI summary
An apparatus for training a reinforcement learning model according to an embodiment includes a starting point determinator configured to determine starting points from an input value of a combinatorial optimization problem, a multi-explorer configured to generate exploration trajectories by performing exploration from each of the starting points using a reinforcement learning model, a trajectory evaluator configured to calculate an evaluation value of each of the exploration trajectories using an evaluation function of the combinatorial optimization problem, a baseline calculator configured to calculate a baseline for the input value from the evaluation value of each exploration trajectory, an advantage calculator configured to calculate an advantage of each of the exploration trajectories using the evaluation value of each exploration trajectory and the baseline, and a parameter updater configured to update parameters of the reinforcement learning model by using the exploration trajectories and the advantages of each exploration trajectory.