Reinforcement learning

Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.

单词	Reward function
释义	Reward function 原声例句问答进行中 I also increased the specificity of the reward function to encourage more detailed movements. 我还增加了奖励函数的特异性，以鼓励更详细的动作。问答进行中 I mean, reward function is is one of the key elements for getting the behavior you want. 我的意思是，奖励函数是获得你想要的行为的关键要素之一。两分钟论文 So researchers at DeepMind decided that they are going to solve this problem with a reward function which is nothing else but forward progress. 所以 DeepMind 的研究者们就决定用奖励函数来解决这一问题，这个函数其实就是前进过程。两分钟论文 To alleviate this, we typically resort to reward engineering, which means that we add additional terms to this reward function to regularize the behavior of these creatures. 为了防止这种情况，一般我们会使用奖励工程的方法，也就是在奖励函数里增加额外的参数来规范数字生物的行为。两分钟论文 This is amazing because it doesn't require any specialized reward function but at the same time, there are a ton of different solutions that get us far in these terrains. 这种方法非常精彩，因为其并不需要独特的奖励函数，不过与此同时，能让我们在这些地形上走很远的解决方案就有些太多了。英语百科 Reinforcement learning Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
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Reward function

Reinforcement learning