Reinforcement Learning

Extracted Attributes

• Field

  Optimal Control

• Analogy

  Similar to animal and human reinforcement learning in behavioral psychology

• Suitability

  Large-scale problems where exact methods are infeasible

• Applications

  Logistics automation

• Key Challenge

  Exploration-exploitation dilemma

• Core Principle

  Maximizing cumulative reward

• Subfield/Concept

  Hierarchical Reinforcement Learning

• Typical Framework

  Markov Decision Process (MDP)

• Key Characteristic

  Operates without an exact mathematical model of the MDP

• Learning Mechanism

  Learning through interaction and feedback (trial and error)

• Distinction from Supervised Learning

  Does not require labeled input-output pairs or explicit error correction

Timeline

• Chris Watkins publishes 'Learning from Delayed Rewards', introducing Q-learning. (Source: Web Search)

  1989-01-01

• Reinforcement Learning is applied to robotics and basic games during the 1990s-2000s. (Source: Web Search)

  1990-01-01

• Sutton & Barto publish 'Reinforcement Learning: An Introduction', a foundational text. (Source: Web Search)

  1998-01-01

• Osaro is founded in the USA, developing deep reinforcement learning technology for robotic systems. (Source: Web Search)

  2015-01-01

• Taranis is founded in the USA, utilizing reinforcement learning for AI-powered crop analytics. (Source: Web Search)

  2015-01-01

• Unbox Robotics is founded in India, developing a logistics automation platform with reinforcement learning. (Source: Web Search)

  2019-01-01

• Latent Technology is founded in the USA, using reinforcement learning for creating dynamic virtual worlds. (Source: Web Search)

  2022-01-01

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 - GeeksforGeeks

  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. * The agent updates its knowledge (policy, value function) based on the reward received and the new state. def get_optimal_path(Q, start, goal, actions, maze, max_steps=200): optimal_path = get_optimal_path(Q, start, goal, actions, maze) * We can observe the total reward trend increasing as the agent learns over time. 8 min readTypes of Machine Learning 7 min readApplications of Machine Learning 9 min readRegularization in Machine Learning 5 min readCross Validation in Machine Learning

• The State of Reinforcement Learning in 2025: Foundations ...

  Reinforcement Learning (RL) has long been one of the most intriguing subfields of artificial intelligence, and in 2025, it continues to push the boundaries of what machines can do — especially in autonomous decision-making, gaming, robotics, and industrial optimization. • 1990s — 2000s: RL applied to robotics and basic games; Sutton & Barto publish Reinforcement Learning: An Introduction (1998). • Machine Learning (ML) → Learning from data. • Reinforcement Learning (RL) → Learning through interaction and feedback. • Meta AI: Known for multi-agent systems and hierarchical RL. • Q-learning • Multi-Agent RL (MARL) • “Learning from Delayed Rewards” — Chris Watkins (1989) [Q-learning] • “Reinforcement Learning: An Introduction” — Sutton & Barto (Book, 1998 / 2018)

• The State of Reinforcement Learning in 2025 - DataRoot Labs

  [Latent Technology](https://www.latent-technology.com/?utm_source=datarootlabs&utm_medium=blog&utm_campaign=research)USA / 2022$2.1M Pre-Seed Spark Capital, Root Ventures Provides technology and tools enabling developers to create dynamic, lifelike virtual worlds through real-time animation, reinforcement learning, and generative modeling techniques.More [Osaro](https://osaro.com/?utm_source=datarootlabs&utm_medium=blog&utm_campaign=research)USA / 2015$96.3M C Octave Ventures LLC, iRobot Develops machine intelligence software based on proprietary deep reinforcement learning technology that enhances computer and robotic systems' efficiency and intelligence, allowing humans to focus on higher-level tasks.More [Taranis](http://www.taranis.com/?utm_source=datarootlabs&utm_medium=blog&utm_campaign=research)USA / 2015$99.6M D iAngels, Vertex Growth Fund Developer of an AI-powered crop analytics platform which uses computer vision, data science, RL, and deep learning algorithms to unlock demand intelligence for agribusiness.More [Unbox Robotics](https://www.unboxrobotics.com/?utm_source=datarootlabs&utm_medium=blog&utm_campaign=research)India / 2019$9.2M Non Equity Assistance Upside Investech Networks Private Limited Developer of a logistics automation platform with reinforcement learning designed to automate and radically improve operations in a limited footprint and capital with a subscription model.More

• 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. Instead, model-free RL algorithms adapt quickly to continuously changing environments and find new strategies to optimize results. The learning process of reinforcement learning (RL) algorithms is similar to animal and human reinforcement learning in the field of behavioral psychology. While supervised learning, unsupervised learning, and reinforcement learning (RL) are all ML algorithms in the field of AI, there are distinctions between the three. While reinforcement learning (RL) applications can potentially change the world, it may not be easy to deploy these algorithms. Amazon Web Services (AWS) has many offerings that help you develop, train, and deploy reinforcement learning (RL) algorithms for real-world applications.

• A Survey on recent advances in reinforcement learning for intelligent ...

  ScienceDirect [Skip to main content](https://www.sciencedirect.com/science/article/pii/S0957417425011625/pdf#main-content) [](https://www.sciencedirect.com/) * Help There was a problem providing the content you requested Please [contact our support team](https://service.elsevier.com/app/contact/supporthub/sciencedirect/) for more information and provide the details below. * **Reference number:**99251211aa015886 * **IP Address:**34.96.49.9 * **User Agent:**Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36 [](https://www.elsevier.com/) * [About ScienceDirect](https://www.elsevier.com/solutions/sciencedirect) * [Remote access](https://www.sciencedirect.com/user/institution/login?targetURL=%2F) * [Shopping cart](https://science-direct-checkout.staging.ecommerce.elsevier.com/?) * [Advertise](https://www.elsmediakits.com/) * [Contact and support](https://service.elsevier.com/app/contact/supporthub/sciencedirect/) * [Terms and conditions](https://www.elsevier.com/legal/elsevier-website-terms-and-conditions) * [Privacy policy](https://www.elsevier.com/legal/privacy-policy) Cookies are used by this site. **Cookie Settings** All content on this site: Copyright © 2024 Elsevier B.V., its licensors, and contributors. For all open access content, the relevant licensing terms apply. [](https://www.relx.com/)

• Instance Of

  Q13433827