Sakenov, NurzhanTyler, B. J.2019-09-062019-09-062019-09http://nur.nu.edu.kz/handle/123456789/4217Abstract—Optimal control and decision making is essential for a wide range of problems, such as resource optimization, realtime scheduling, making decisions based on inaccurate or incomplete data, and reasoning under uncertainty. Examples of such problems include allocation of resources during wildfires, theater missile defense or control of unmanned combat aerial vehicles. One approach to these kinds of problems is adaptive intelligent agents. When mathematically optimal exact solutions are not suitable (for example, dynamic programming is slow, offline and requires perfect knowledge), adaptive agents can approximate optimality with greater speed and ability to handle uncertainty and limited knowledge. Furthermore, using a distributed decision making approach can improve robustness of the overall system in question. In this paper, we will investigate several approaches to designing intelligent agents using neural networks. Neuro-dynamic programming (NDP) is a method of approximate dynamic programming using neural networks. Neuro-Fuzzy dynamic programming (NFDP) is a variation of NDP with incorporated fuzzy logic. There are other methods, such as Genetic Fuzzy Trees and Neuro-Fuzzy Inference Systems. They all differ in their ways of handling the state space and minimizing the relevant objectives. In this paper, we will look at several problems that people have tried to solve using these approaches. We will discuss the types of these problems and features that make these problems well-suited for adaptive agents.enAttribution-NonCommercial-ShareAlike 3.0 United Statesoptimal controladaptive agentsneuro-dynamic programmingreinforcement learningsurveyOther