SIG796 - Reinforcement Learning
| Year: | 2026 unit information |
|---|---|
| Offering information: | Available from 2027 |
| Enrolment modes: | Trimester 1: Great Learning |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | Nil |
| Corequisite: | Must be enrolled in S732 Master of Applied Artificial Intelligence (Global) |
| Incompatible with: | SIT796 |
| Study commitment: | Students will on average spend 150 hours over the trimester undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - online: | Online independent and collaborative learning including optional scheduled activities as detailed via the Great Learning platform. |
| Note: | This unit is part of the Master of Applied Artificial Intelligence (Global) program and is restricted to online international students who reside outside Australia. |
Content
Reinforcement Learning (RL) is one of the three fundamental paradigms of Machine Learning that is inspired by psychology and neuroscience and is concerned with the development of software agents capable of taking actions in an environment to achieve one or more goals. RL differs from supervised learning in that it does not require correct input/output pairs and incorrect actions do not need to be directly corrected, instead agents balance exploration and exploitation to search for optimal policies. In this unit students will explore, research and implement solutions to a range of RL problems or Markov Decision Process (MDP), including variants such as: discrete-time MDP; Semi-MDPs (SMDP); continuous-time MDPs; Partially Observable-MDP (POMDP) and Multi-objective MDPs (MOMDP). In solving these problems, students will apply their knowledge and skills with a range of techniques such as: multi-armed bandits; reward design; value iteration; policy gradient; temporal difference learning; on-policy; off-policy; eligibility traces; feature construction; and, continuous action. Additionally, students will research and discuss topics such as; curriculum; interactive; inverse; transfer; ensemble; hierarchical; curiosity, multi-goal; multi-agent; multi-objective and deep RL.
Hurdle requirements
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the Learning Portfolio.