This thesis is the research and development of intelligent coordination strategies that can be integrated into models for hierarchical neuro-fuzzy systems of multiple agents in complex environments. In dynamic environments or complex organization of agents must adapt to changes in the objectives of the system, availability of resources, relationships between agents, and so on. This flexibility is a key problem in multiagent systems. The main objective of the proposed models is to make multiple agents interact intelligently with each other in complex systems. In this work we developed two new intelligent neuro-fuzzy models with hierarchical coordination mechanism for multi-agent systems, namely Neuro-Fuzzy Model with Hierarchical Reinforcement Learning with coordination mechanism Market-Driven (RL-NFHP-MA-MD), and Neuro-Fuzzy model with Hierarchical Reinforcement Learning with coordination model for graphs (RL-NFHP-MA-CG). The inclusion of coordination models to model with Neuro-Fuzzy Hierarchical Reinforcement Learning (RL-NHFP-MA) was primarily motivated by the importance of optimizing the performance of the work in all players, improving the model results and targeting more complex applications. The models were designed based on the study of existing limitations in current models and desirable features for learning systems based RL, in particular when applied to continuous environments and/or environments considered large. The developed models were tested primarily through two case studies: application benchmark game of predator-prey ( Pursuit-Game) and Soccer robots (simulated and robotic agents). The results obtained both in the game of predator-prey as in soccer robot through new models RL-NFHP-MA-MD and RL-NFHP-MA-CG for multiple agents proved promising. The tests showed that the new system showed ability to coordinate actions among agents with a convergence rate nearly 30 percent higher than the original version. Results soccer robot were obtained with model RL-NFHP-MA-MD–NFHP-RL and model-CG-MA, the results are good in games played in full as specific winning teams developed with other similar models.