The aim of research: Systematic comparison of eight decentralized training baselines. We are inspired by the research of , where authors tested choosing best clearing action with Deep Learning on fire spread simulation. However, authors provide limited choice of algorithms with limited metrics and encounter non-stationairty issues due to common reward. Focus of our research is evaluation through extensive benchmarking of Independent, value-decomposition, central-critic, and agent-modeling methods proposed by Papoudakis et. al evaluated under common hardware/runtime constraints. Our work considers constraints of partial observability, generalization and mixed teams. Results promote insights on beneficiary features of baselines to assist further researches in selecting or developing effective algorithms for decen- tralized planning and control. Our contribution transfers Wildfire benchmark, created by Tran Research Group to PettingZoo library to promote verification of our results.