Reinforcement learning

type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards or penalties in return, aiming to maximize the cumulative reward over time

'''Q-learning''' is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment (model-free). It can handle problems with stochastic transitions and rewards without requiring adaptations.

variant of reinforcement learning

intelligent Tutoring System

techniques combining deep learning and reinforcement learning principles to create efficient machine learning algorithms

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote.

machine-learned bot project using the video game Dota 2

model-free reinforcement learning algorithm

sub-field of reinforcement learning

type of machine learning algorithm