با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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1 |
Improved reinforcement learning with curriculum
بهبود یادگیری تقویتی با برنامه درسی-2020 Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner.
For instance, when learning how to play a board game, usually one of the first concepts learned is how the
game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning endgames
first is that once the actions leading to a terminal state are understood, it becomes possible to
incrementally learn the consequences of actions that are further away from a terminal state – we call this
an end-game-first curriculum. The state-of-the-art machine learning player for general board games,
AlphaZero by Google DeepMind, does not employ a structured training curriculum. Whilst Deepmind’s
approach is effective, their method for generating experiences by self-play is resource intensive, costing
literally millions of dollars in computational resources. We have developed a new method called the endgame-
first training curriculum, which, when applied to the self-play/experience-generation loop, reduces
the required computational resources to achieve the same level of learning. Our approach improves performance
by not generating experiences which are expected to be of low training value. The end-gamefirst
curriculum enables significant savings in processing resources and is potentially applicable to other
problems that can be framed in terms of a game. Keywords: Curriculum learning | Reinforcement learning | Monte Carlo tree search | General game playing |
مقاله انگلیسی |
2 |
Genetic state-grouping algorithm for deep reinforcement learning
الگوریتم گروه بندی حالت ژنتیکی برای یادگیری تقویتی عمیق-2020 Although Reinforcement learning has already been considered one of the most important and wellknown
techniques of machine learning, its applicability remains limited in the real-world problems
due to its long initial learning time and unstable learning. Especially, the problem of an overwhelming
number of the branching factors under real-time constraint still stays unconquered, demanding a new
method for the next generation of reinforcement learning. In this paper, we propose Genetic State-
Grouping Algorithm based on deep reinforcement learning. The core idea is to divide the entire set of
states into a few state groups. Each group consists of states that are mutually similar, thus representing
their common features. The state groups are then processed with the Genetic Optimizer, which finds outstanding
actions. These steps help the Deep Q Network avoid excessive exploration, thereby contributing
to the significant reduction of initial learning time. The experiment on the real-time fighting video game
(FightingICE) shows the effectiveness of our proposed approach. Keywords: Reinforcement learning | Genetic algorithm | Hybrid method | Monte Carlo Tree Search | Game AI |
مقاله انگلیسی |