Comparison of strategy learning methods in Farmer–Pest problem for various complexity environments without delays

In this paper effectiveness of several agent strategy learning algorithms is compared in a new multi-agent Farmer–Pest learning environment. Learning is often utilized by multi-agent systems which can deal with complex problems by means of their decentralized approach. With a number of learning methods available, a need for their comparison arises. This is why we designed and implemented new multi-dimensional Farmer–Pest problem domain, which is suitable for benchmarking learning algorithms. This paper presents comparison results for reinforcement learning (SARSA) and supervised learning (Naïve Bayes, C4.5 and Ripper). These algorithms are tested on configurations with various complexity with not delayed rewards. The results show that algorithm performances depend highly on the environment configuration and various conditions favor different learning algorithms.

► We propose the multi-dimensional domain allowing comparison of learning algorithms. ► We compare efficaciousness of several agent strategy learning algorithms in the proposed domain. ► We show that methods other than reinforcement learning can be used for agent strategy generation. ► We show that in specific conditions, supervised learning can improve performance of agents much faster that reinforcement learning.