Open Access
2- https://doi.org/10.47611/jsrhs.v10i4.2143
Linear Function Approximation as a Resource Efficient Method to Solve the Travelling Salesman Problem
- Jun 10, 2022
- Journal of Student Research
- Rolan Guang +1 more
Abstract
This paper presents an approach to combinatorial optimization problems using linear function approximation (LFA) to solve the Travelling Salesman Problem (TSP). We create a reinforcement learning model in which we parameterize our policy using linear function approximation instead of the more commonly used neural networks. We then evaluated our models based on two factors: training time and optimality. When we compared our results with a state-of-the-art neural network solver, we found that our model was able to solve the TSP accurately while using drastically less computational resources and time to train than the neural network algorithm (Kool et al., 2019).
- # Linear Function Approximation
- # Travelling Salesman Problem
- # Neural Network Solver
- # Linear Approximation
- # Neural Network Algorithm
- # Neural Network
- # Training Time
- # Computational Resources
- # Computational Time
- # Reinforcement Learning Model
