FIT3233 Optimisation and reinforcement learning

Many artificial intelligence systems must do more than recognise patterns: they must choose actions, optimise outcomes and adapt through interaction with changing environments. This unit develops advanced capability in optimisation and reinforcement learning, building on prior deep learning knowledge to explore how intelligent systems can learn to make decisions under uncertainty.

You will study optimisation methods used in artificial intelligence and machine learning, including objective functions, constraints, gradient-based optimisation, stochastic optimisation, hyperparameter optimisation and trade-offs between exploration, exploitation, performance and efficiency. You will also examine reinforcement learning foundations, including agents, environments, rewards, policies, value functions, temporal-difference learning, policy gradients, deep reinforcement learning and evaluation of learned behaviour.

You will formulate optimisation and sequential decision-making problems, implement and evaluate reinforcement learning approaches, and analyse the behaviour, limitations and performance of artificial intelligence systems. The unit emphasises informed judgement in selecting methods, designing reward structures, interpreting experimental results and communicating the strengths, risks and limitations of optimisation and reinforcement learning solutions.