Module Specifications..
Current Academic Year 2023 - 2024
Please note that this information is subject to change.
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Repeat examination |
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Description This module is accredited by NUIG. The topic of Reinforcement Learning & Multi-Agent Systems, examines environment that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal. A typical agent environment could be a trading environment where an agent attempts to optimise energy usage, or the profitability of a transaction. More recently, significant global attention has focussed on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals. This module begins by examining the underpinnings of what is an Agent, and how we can better understand the principles of an agent and its autonomy. Multi-Agent Systems are then explored, as a means of understanding how many agents can interact with each other in a complex environment. Agents are commonly modelled using Game Theory, and in this module a range of Game Theoretic Models will be studied. The module will examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge. The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples in discrete environments and how to formulate a reinforcement learning task. It then extends this to continuous problem spaces, detailing Deep Reinforcement Learning with a practical implementation of a Deep Q Network using Keras. Further information pertaining to the module is available from NUIG. | |||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Explain and discuss the principles underlying Agents 2. Explain the role of game theory and games in agent design. 3. Apply the principle of agents to a range of simulation problems. 4. Understand the theory unpinning reinforcement learning. 5. Apply reinforcement learning to a real-world problem. 6. Apply advanced deep reinforcement learning approaches to a real-world problem. | |||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
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Other Resources None | |||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes
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