Reinforcement Learning (RL)

Extracted Attributes

• Goal

  Maximize cumulative reward

• Field

  Optimal Control

• Paradigm

  One of three basic machine learning paradigms

• Capability

  Delayed gratification

• Connection

  Connects data-driven perception to adaptive control

• Core Challenge

  Exploration-exploitation dilemma

• Feedback Mechanism

  Reward-and-punishment paradigm

• Learning Mechanism

  Trial-and-error

• Environment Modeling

  Markov decision process

• Algorithm Characteristic

  Targets large Markov Decision Processes where exact methods are infeasible

• Distinction from Supervised Learning

  Does not need sub-optimal actions to be explicitly corrected

• Alternative Name (Operations Research/Control Literature)

  Neuro-dynamic programming

Reinforcement learning

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process, as many reinforcement learning algorithms use dynamic programming techniques. The main difference between classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the Markov decision process, and they target large Markov decision processes where exact methods become infeasible.

Web Search Results

• Reinforcement learning - Wikipedia

  Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. [...] Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. In the operations research and control literature, RL is called approximate dynamic programming, or neuro-dynamic programming. The problems of interest in RL have also been studied in the theory of optimal control, which is concerned mostly with the [...] In recent years, reinforcement learning has become a significant concept in natural language processing (NLP), where tasks are often sequential decision-making rather than static classification. Reinforcement learning is where an agent take actions in an environment to maximize the accumulation of rewards. This framework is best fit for many NLP tasks, including dialogue generation, text summarization, and machine translation, where the quality of the output depends on optimizing long-term or

• What is Reinforcement Learning? - AWS

  Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals. Software actions that work towards your goal are reinforced, while actions that detract from the goal are ignored. [...] The learning process of reinforcement learning (RL) algorithms is similar to animal and human reinforcement learning in the field of behavioral psychology. For instance, a child may discover that they receive parental praise when they help a sibling or clean but receive negative reactions when they throw toys or yell. Soon, the child learns which combination of activities results in the end reward. [...] RL algorithms use a reward-and-punishment paradigm as they process data. They learn from the feedback of each action and self-discover the best processing paths to achieve final outcomes. The algorithms are also capable of delayed gratification. The best overall strategy may require short-term sacrifices, so the best approach they discover may include some punishments or backtracking along the way. RL is a powerful method to help artificial intelligence (AI) systems achieve optimal outcomes in

• Reinforcement Learning - GeeksforGeeks

  # Reinforcement Learning Last Updated : 15 Sep, 2025 Suggest changes 138 Likes Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize cumulative rewards. RL allows machines to learn by interacting with an environment and receiving feedback based on their actions. This feedback comes in the form of rewards or penalties. [...] Reinforcement Learning revolves around the idea that an agent (the learner or decision-maker) interacts with an environment to achieve a goal. The agent performs actions and receives feedback to optimize its decision-making over time.

• What is reinforcement learning? - IBM

  Reinforcement learning (RL) is a type of machine learning process that focuses on decision making by autonomous agents. An autonomous agent is any system that can make decisions and act in response to its environment independent of direct instruction by a human user. Robots and self-driving cars are examples of autonomous agents. In reinforcement learning, an autonomous agent learns to perform a task by trial and error in the absence of any guidance from a human user.1 It particularly addresses

• Reinforcement Learning Guide: Algorithms, Applications ... - Lightly

  What is reinforcement learning, and how does it work? Reinforcement learning (RL) is a machine learning method where an agent learns by interacting with its environment. It takes actions, receives rewards, and refines its strategy over time to maximize long-term gains through trial and error. How is reinforcement learning different from supervised or unsupervised learning? [...] ### Share blog post Reinforcement Learning (RL) is a machine learning approach where agents learn to make decisions via trial and error. By interacting with their environment and receiving rewards, they improve over time to achieve long-term goals in tasks like robotics, games, and more. Ideal For: CV and AI Engineers Reading time: 10 mins Category: Models #### Share blog post Below, you can find a quick summary of key points about reinforcement learning TL;DR [...] Reinforcement learning (RL) connects data-driven perception to adaptive control. It allows agents to learn action policies through direct interaction and feedback. You can use RL to adapt models to unpredictable inputs where traditional supervised learning falls short. This guide helps you apply RL to real-world projects where models need to learn and adapt independently. In this guide, we will cover: