mimo

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.

It s genetic operator (crossover and mutation) for discrete optimization

油田规划数学建模的matlab程序，我自己建模写的。(Mathematical modeling of oil field matlab program, written in my own modeling.)

Ensure the default values are set for any receiver, activity, service, etc. of Calendar.

Solve Equation by newton rofson method for students to solve own problems.

this script plots the velocity profile for user defined function.and user can generate multiple colour curves.suggestion included to do this.

说明：  整数线性规划中intprog的代码定义，用于matlab中(integer linear programming)

MATLAB的设计，包括常见的函数和功能(MATLAB design, including the common functions and features)

剪切图片，有利于初学者，希望能够帮助有些人(cut picture)

这是一个（2.1.3）卷积码编码、BSC信道传输、Viterbi译码组成的程序合集，运行main.m即可得到仿真结果(This is a (2.1.3) convolutional code, Viterbi decoding, BSC channel transmission is composed of collection procedures, running main.m can be obtained by the simulation results)