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Material Detail

Reinforcement Learning – Learning by Trial and Feedback

Reinforcement Learning – Learning by Trial and Feedback

This page introduces reinforcement learning, where an agent learns to act in an environment by receiving rewards and penalties. It explains key concepts such as states, actions, policies, and value functions, and highlights applications in games, robotics, and operations.

Keywords:
decision-making, game AI, Markov decision processes, optimisation, Prep4Uni.online, AI, RL, robotics, agents, machine learning, reinforcement learning
Disciplines:
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More about this material

Material Type:
Reference Material
Date Added to MERLOT:
December 14, 2025
Date Modified in MERLOT:
December 14, 2025
Author:
Unknown (Know the author?)
Submitter:
Jacob Gan
Primary Audience:
High School, College General Ed, College Lower Division, College Upper Division, Graduate School, Professional
Technical Format:
Website
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
Yes
Creative Commons:
Creative Commons License
This work is licensed under a CC0 1.0 Universal

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