{"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 <\/p>\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 <\/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 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 Reinforcement learning involves the following key elements:<\/p>\n Additionally, algorithms<\/strong><\/a> are an integral part of the RL process and come into play in various steps. They are used to design the learning agent\u2014i.e, its decision-making process, how it updates its policy, and how it learns from the feedback received.<\/p>\n At the heart of reinforcement learning lies the concept of reinforcing optimal behavior or action through a reward system. Engineers come up with a method of rewarding desired behaviors and punishing unwanted behaviors.<\/p>\n They also employ various techniques to prevent short-term rewards from stalling the agent, delaying the achievement of the overall objective. This means defining rewards that align with the long-term objective so that the agent learns to prioritize actions that lead to the desired outcome.<\/p>\n Reinforcement learning is an iterative cycle of exploration, feedback, and improvement. The process can be better understood through this workflow:<\/p>\n As an example, let\u2019s apply the RL workflow to a robotic vacuum cleaner:<\/p>\n Reinforcement learning is a distinct approach to machine learning that significantly differs from the other two main approaches.<\/p>\nWhat is reinforcement learning?<\/h2>\n
The elements of reinforcement learning<\/h3>\n
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How does reinforcement learning work?<\/h2>\n
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Reinforcement learning compared to other methods<\/h2>\n
Supervised learning vs. reinforcement learning<\/h3>\n