Key facts
UNE unit code: COSC552
*You are viewing the 2025 version of this unit which may be subject to change in future.
- Trimester 1 - On Campus
- Trimester 1 - Online
- Armidale Campus
- Yes
- No
- No
- 6
Unit information
Reinforcement Learning (RL) is an essential topic in the machine learning paradigm. Supervised and unsupervised learning approaches establish a decision function based on an example dataset. Reinforcement learning does not require an example dataset, but instead, determines an optimal policy based on a set of rules and its interaction with an environment. This unit will explored both model-based and model-free examples, including the Monte Carlo Decision Process (MDP), Q-Learning, Temporal Difference (TD) and Dynamic Programming. Later we will explored both conventional and deep learning techniques used in Reinforcement Learning. Upon completion of this unit students will be able to adapt a range of tools to stochastic problems in machine learning with an understanding of the taxonomy and while working with practical examples.
Offerings
For further information about UNE's teaching periods, please go to Principal Dates.
Teaching period | Mode/location |
---|---|
Trimester 1 | On Campus, Armidale Campus |
Trimester 1 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Please refer to the student handbook for current details on this unit.
Unit coordinator(s)
Learning outcomes
Upon completion of this unit, students will be able to:
- expertly demonstrate an understanding of concepts and taxonomies used in Reinforcement Learning;
- evaluate and compare Reinforcement Learning problems in machine learning and propose advanced solutions;
- adapt tools to stochastic environments to solve complex real-world problems;
- implement and optimise algorithms using a tool box approach in a high-level programming language;
- design and implement efficient solutions in a high-level programming language; and
- analyse and report the difficulties in solving complex reinforcement learning problems.
Assessment information
Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Title | Must Complete | Weight | Offerings | Assessment Notes |
---|---|---|---|---|
Practical Assessment | Yes | 10% | All offerings | Adapt model-free solutions in Reinforcement Learning to a practical example. |
Practical Assessment | Yes | 20% | All offerings | Adapt model-based solutions in Reinforcement Learning to a practical example. |
Quizzes | Yes | 10% | All offerings | 5 quizzes worth 2% each covering key concepts in Reinforcement Learning. |
Theory Assignment | Yes | 10% | All offerings | Additional theory assignment quiz for postgraduate level. |
Final Examination | Yes | 50% | All offerings | It is mandatory to pass this component in order to pass the unit. |
Learning resources
Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Note: Students are expected to purchase prescribed material. Please note that textbook requirements may vary from one teaching period to the next.
Reinforcement Learning: An Introduction
ISBN: 9780262039246
Sutton, R., and Barto, A., Random House 2nd 2018
Text refers to: All offerings
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