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AUTONOMOUS AGENTS IN GAMES

CODE 98216
ACADEMIC YEAR 2022/2023
CREDITS
  • 5 cfu during the 2nd year of 8732 INGEGNERIA ELETTRONICA (LM-29) - GENOVA
  • 10 cfu during the 2nd year of 10728 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY)(LM/DS) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/01
    LANGUAGE Italian (English on demand)
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course presents algorthms for autonomous intelligent agents that interact with an unknown environment. In particular, the environment is simulated by a virtual world created through video games technology. The course will introduce the Reinforcement Learning e del Deep Reinforcement Learning approaches.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The course provides algorithms and strategies to develop autonomous agents using a game engine.

    AIMS AND LEARNING OUTCOMES

    The aim of the course is to provide the basis for the design and development of software algorithms capable of autonomously acting within a virtual world. The student is introduced to different concepts of Reinforcement Learning and supported through extensive exercises during lectures. The course aims to train a professional figure capable of designing and implementing complex software applications using video game technologies and artificial intelligence algorithms.

    PREREQUISITES

    The students should have basic knowledge of programming and machine learning.

    TEACHING METHODS

    The course is composed of a set of frontal lessons and a set of practice sessions. During the frontal lesson, the teacher presents the topics providing also examples of live code that are tested on simualted environements. Students can use their own laptops during the lecture in order to reproduce what is proposed by the teacher. During the practice sessions, the students have to face up with real problems that they should solve by applying the techniques learnied during the lectures.

    SYLLABUS/CONTENT

    The titles of the main contents discussed during frontal lessons are provided in the following list. Notebooks can be obtained at the following lins: Reinforcement Learning and Deep Reinforcement Learning

    01 - Markov Decision Process
    02 - Dynamic Programming
    03 - Exploration vs Exploitation
    04 - Policy Evalutaion
    05 - Policy Improvement
    06 - Learning and Planning
    07 - Function Approximation
    08 - Deep Q-network
    09 - Actor-Critic methods
    10 - DDPG and PPO

    RECOMMENDED READING/BIBLIOGRAPHY

    • Lecture notes and notebooks (from Github)
    • Books (as references):
      - Miguel Morales, Grokking Deep Reinforcement Learning, Manning
      - Richard S. Sutton, Andrew G. Barto, Reinforcement Learning: an Introduction, MIT Press

    TEACHERS AND EXAM BOARD

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The exam is an oral examination on the theoretical topics covered during lectures or a review of a recent scientific paper. In particular, the student has to provide fluency in the description of the main concept of autonomous agents development.

    ASSESSMENT METHODS

    During the oral exam, the teacher asks the student to illustrate some concepts learned in class. For each concept, the student has to present the definition, the conditions of applicability and pros/cons in relation to other approaches. During the examination, the teacher verifies that the concepts have been learned at a level of knowledge that allows the student to apply them in real cases.

    Exam schedule

    Date Time Location Type Notes