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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Reinforcement Learn & MultiAgent Systm (NUIG)
Module Code CA6006I
School School of Computing
Module Co-ordinatorSemester 1: Annalina Caputo
Semester 2: Annalina Caputo
Autumn: Annalina Caputo
Module TeachersAnnalina Caputo
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination

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.

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.

Workload Full-time hours per semester
Type Hours Description
Total Workload: 0

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

Indicative Content and Learning Activities

Assessment Breakdown
Continuous Assessment30% Examination Weight70%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 3
Indicative Reading List

    Other Resources

    Programme or List of Programmes
    MCMM.Sc. in Computing

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