Reinforcement Learning

Reinforcement-Learning

Reinforcement Learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with its environment. The agent takes actions and receives feedback in the form of rewards or penalties, which guide its learning process. Unlike supervised learning, RL does not require labeled data; instead, it relies on trial and error to discover optimal strategies. This approach is widely used in various applications, including robotics, gaming, and healthcare, where agents can learn complex behaviors and improve their performance over time. RL’s ability to adapt and learn from experience makes it a powerful tool in artificial intelligence.

Reinforcement Learning:

Analytics Vidhya

Reinforcement learning is a field of machine learning in which an agent is learn by its own with the help of actions and their rewards. This is a method in which there is no need of supervision of…

Towards Data Science

Reinforcement learning(RL) is a type of deep learning that has been receiving a lot of attention in the past few years. It is useful for the situations we want to train AI for certain skills we don’t…...

Reinforcement Learning & Sushi Go!

Towards Data Science

Reinforcement learning is an area of machine learning concerned with how an agent takes action based on its environment to maximize its long-term reward. Although the concept has been out for a…

Getting Started with Reinforcement Learning

Analytics Vidhya

Reinforcement Learning is a type of Machine Learning that uses dynamic programming to train algorithms to learn its environment and perform a specific task using a system of reward and punishment. A…

Reinforcement Learning

Dive intro Deep Learning Book

Pratik Chaudhari ( University of Pennsylvania and Amazon ), Rasool Fakoor ( Amazon ), and Kavosh Asadi ( Amazon ) Reinforcement Learning (RL) is a suite of techniques that allows us to build machine l...

Reinforcement Learning — AI and Machine Learning

Towards Data Science

Reinforcement learning is a field of Machine Learning where software agents in order to solve a particular problem takes action in an uncertain and potentially complex environment. Through these…

Reinforcement Learning

Machine Learning Glossary

Reinforcement Learning In machine learning, supervised is sometimes contrasted with unsupervised learning. This is a useful distinction, but there are some problem domains that have share characterist...

April Edition: Reinforcement Learning

Towards Data Science

Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games…...

Reinforcement Learning — Teaching the Machine to Gamble with Q-learning

Level Up Coding

Reinforcement Learning is an area of Artificial Intelligence and Machine Learning that involves simulating many scenarios in order to optimize the outcomes. One of the most used approaches in…

Reinforcement Learning (Q-Learning) with Decision Trees

Towards Data Science

Reinforcement learning (RL) is a paradigm in machine learning where a computer learns to perform tasks such as driving a vehicle, playing atari games, and beating humans in the game of Go, with…

Introduction of Reinforcement Learning- Q & A

Analytics Vidhya

Reinforcement Learning is machine learning technique where an agent learns from the environment using trial and error through maximising the cumulative reward for actions taken to achieve the defined…...

An Illustrated Overview of Reinforcement Learning

Analytics Vidhya

Reinforcement Learning (RL) is a learning paradigm different from traditional machine learning (supervised and unsupervised). The learning problem considered here mimics humans learning from…