Optimizing Neural Network Performance in Game Playing Using Simulated Annealing and Reinforcement Learning
Year: 2024
Type: conference-paper
Abstract: This paper experiments optimization for Neural Network (NN) parameters for game playing using Simulated Annealing (SA) and Reinforcement Learning (RL). The study focuses on the Dino Game, comparing the performance of the proposed NN method against a baseline Decision Tree method. Experimental results demonstrate that the NN outperforms the Decision Tree, achieving a higher mean score with greater consistency. Statistical tests confirm the performance improvements are statistically significant, indicating the effectiveness of the SA heuristic in optimizing NN parameters.
Institution: Universidade Federal do Espírito Santo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404927925