reinforcement learning (RL)

Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. It is characterized by the agent’s ability to learn optimal behaviors through trial and error, receiving rewards or penalties for actions taken. Unlike supervised learning, RL does not require labeled input/output pairs; instead, it relies on the feedback from the environment to inform the agent of the quality of its actions. The process involves balancing exploration (trying new actions) with exploitation (using known actions that yield high rewards). RL is often modeled as a Markov decision process and uses various algorithms, such as Q-learning or policy gradients, to learn the value of actions and to determine the best policy for action selection[1][2][3][4].

Citations:

[1] https://en.wikipedia.org/wiki/Reinforcement_learning

[2] https://www.synopsys.com/ai/what-is-reinforcement-learning.html

[3] https://www.geeksforgeeks.org/what-is-reinforcement-learning/

[4] https://aws.amazon.com/what-is/reinforcement-learning/

[5] https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-reinforcement-learning/amp/

[6] https://www.sciencedirect.com/topics/computer-science/reinforcement-learning

[7] https://www.techtarget.com/searchenterpriseai/definition/reinforcement-learning