In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling.
To get you started thinking algorithmically about the Explore-Exploit dilemma, in computer science, a greedy algorithm is an algorithm that always takes whatever action seems best at the present moment, even when that decision might lead to bad long term consequences. The epsilon-Greedy algorithm is almost a greedy algorithm because it generally exploits the best available option, but every once in a while the algorithm explores the other available options.
Quando os números aparecem na parte inferior indica alta, quando aparece na parte superior indica baixa, quando aparece no meio do candlestick indica continuidade do movimento.Greedy Algorithm
Program for Greedy Algorithm to find Minimum number of Coins.
Esse trabalho foi feito pensando em como se deve usar a Orientação a Objetos em nossos trabalhos.Accelerator Oscillator (AC)
O Indicador Acceleration/Deceleration (Aceleração/Desaceleração ou AC) mede a aceleração e a desaceleração da força motriz atual do mercado.