{"id":529172,"date":"2023-07-14T12:28:05","date_gmt":"2023-07-14T10:28:05","guid":{"rendered":"https:\/\/www.scribbr.nl\/?p=529172"},"modified":"2023-08-15T17:06:38","modified_gmt":"2023-08-15T15:06:38","slug":"reinforcement-learning","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/ai-tools\/reinforcement-learning\/","title":{"rendered":"Easy Introduction to Reinforcement Learning"},"content":{"rendered":"

Reinforcement learning (RL) <\/strong>is a branch of machine learning<\/strong><\/a> that focuses on training computers to make optimal decisions by interacting with their environment. Instead of being given explicit instructions, the computer learns through trial and error: by exploring the environment and receiving rewards or punishments for its actions.<\/p>\n

Together with supervised<\/strong> and unsupervised learning<\/strong><\/a>, reinforcement learning is one of three basic machine learning approaches. Reinforcement learning has a wide range of real-world applications, including robotics, game playing, and diagnosing rare diseases.<\/p>\n

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What is reinforcement learning?<\/h2>\n

Reinforcement learning (RL) is a way for computers to learn independently by making a series of decisions and learning from the outcomes. Through trial and error, computer programs determine the best actions within a certain context and optimize their performance.<\/p>\n

The computer receives positive or negative feedback based on its actions and gradually learns how to complete a task. In other words, RL is about learning the optimal behavior in an environment to obtain maximum reward.<\/p>\n

\"What<\/p>\n

RL is an approach suitable for addressing problems involving a series of decisions that all affect one another.<\/p>\n

Training a computer to win at backgammon, for example, involves a whole sequence of good decisions, not just one. In games like this, there are several possible actions and scenarios, and a lot of uncertainty regarding how short-term actions pay off in the long run. RL can also help solve complex problems of control, such as walking robots or self-driving cars.<\/p>\n

Unlike the other two learning frameworks, which operate on the basis of an existing dataset, RL gathers data as it interacts with its environment. It allows a piece of software to find the optimal solution by exploring, interacting with, and ultimately learning from the environment.<\/p>\n

Note<\/figcaption>Reinforcement learning from human feedback (RLHF)<\/strong> is a machine learning<\/a> approach that combines reinforcement learning techniques (i..e., positive and negative feedback) with human guidance to improve the learning process. It is applied to various domains of natural language processing, like ChatGPT.<\/p>\n

In RLHF, a pre-trained language model (e.g., a chatbot) is assessed by humans, who score the responses it generates. By incorporating human feedback, experts can direct the model to favor certain outputs over others\u2014for example, those that read more naturally or are more helpful.<\/figure>\n

The elements of reinforcement learning<\/h3>\n

Reinforcement learning involves the following key elements:<\/p>\n