This paper concerns a novel auxiliary model based expectation maximization (EM) estimation method for Hammerstein systems with data loss by extending the EM method to estimate models with multiple parameter vectors. The novel EM method relaxes the requirements on an autoregression model with one parameter vector, interactively maximizes the expectation over multiple parameter vectors in a more general model, and uses the output of an auxiliary model to substitute the missing outputs in the information vector in iteration processes. A numerical simulation is employed to demonstrate the effectiveness of the proposed novel EM method.

Multiagent reinforcement learning (MARL) has been extensively used in many applications for its tractable implementation and task distribution. Learning automata, which can be classified under MARL in the category of independent learner, are used to obtain the optimal joint action or some type of equilibrium. Learning automata have the following advantages. First, learning automata do not require any agent to observe the action of any other agent. Second, learning automata are simple in structure and easy to be implemented. Learning automata have been applied to function optimization, image processing, data clustering, recommender systems, and wireless sensor networks. However, a few learning automata-based algorithms have been proposed for optimization of cooperative repeated games and stochastic games. We propose an algorithm known as learning automata for optimization of cooperative agents (LA-OCA). To make learning automata applicable to cooperative tasks, we transform the environment to a P-model by introducing an indicator variable whose value is one when the maximal reward is obtained and is zero otherwise. Theoretical analysis shows that all the strict optimal joint actions are stable critical points of the model of LA-OCA in cooperative repeated games with an arbitrary finite number of players and actions. Simulation results show that LA-OCA obtains the pure optimal joint strategy with a success rate of 100% in all of the three cooperative tasks and outperforms the other algorithms in terms of learning speed.

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.