42715 Sustainable Judgements
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 3 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Description
The subject introduces reinforcement learning, or in simple terms, how machines learn by interacting with their environment, something human beings do from birth. Reinforcement learning is a subfield of machine learning but is also a general-purpose formalism for automated decision-making and AI. It deals with building programs that learn how to predict and act in a stochastic environment, based on past-experience, or interaction with the environment. Applications of reinforcement learning range from classical control problems, such as power-plant?optimisation?or dynamical system control, to game playing, inventory control, and many other fields.
In this subject, students will study the theoretical aspects and practical applications of reinforcement learning.?State-of-the art software tools are discussed and used for the implementation of RL algorithms and training intelligent agents. Students learn how to formalise problems as Markov Decision Processes and learn classic and modern algorithms in reinforcement learning. Students learn to implement, train, and test their own RL agent. After the completion of the subject, students will understand value functions, basic exploration methods, and the comparison between exploration and exploitation methods. Finally, student will explore how to deploy trained agents and build an AI system that solves a real-world problem in the final project.
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition. This is available for free here and references will refer to the final pdf version available here.
Recommended texts
- Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn van Otterlo, Eds. [link]
- David Silver's course on Reinforcement Learning [link]
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. [link]
- Dive into Deep Learning (https://d2l.ai/index.html)