Comparison of Reinforcement and Supervised Learning Methods in Farmer-Pest Problem with Delayed Rewards

摘要

In this paper we propose a method based on the time-window idea which allows agents to generate their strategy using supervised learning algorithms in environments with delayed rewards. It is universal and can be used in various environments. Learning speed of the proposed method and reinforcement learning algorithm are compared in a Farmer- Pest problem with delayed rewards. Farmer-Pest problem is chosen for the comparison because it is designed especially for learning algorithms benchmarking. It has several dimensions which change environment characteristics and allows to test algorithms in various conditions. This paper presents results for one reinforcement learning method (SARSA) and three supervised learning algorithms (Na?ve Bayes, C4.5 and Ripper). These algorithms are tested on configurations with various complexity.