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| <!DOCTYPE html> | ||
| <html lang="en"> | ||
| <head> | ||
| <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> | ||
| <title>Dr Timothy Wiley - Students</title> | ||
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| <link rel="stylesheet" type="text/css" href="styles/common.css" /> | ||
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| <link rel="stylesheet" type="text/css" href="styles/publications.css" /> | ||
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| <link rel="shortcut icon" href="images/logo.png"> | ||
| </head> | ||
| <body> | ||
| <header> | ||
| <div class="container"> | ||
| <h1>Dr Timothy Wiley</h1> | ||
| <h5 id="quals">PhD, BSc (Computer Science)</h5> | ||
| <div id="subinfo">Lecturer</div> | ||
| <div id="subinfo">School of Computing Technologies, STEM College</div> | ||
| <div id="subinfo">RMIT University</div> | ||
| </div> | ||
| </header> | ||
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| <nav> | ||
| <ul> | ||
| <li><a class="" href="index.html">Home</a></li> | ||
| <li><a class="" href="robocup.html">RoboCup</a></li> | ||
| <li><a class="" href="publications.html">Publications</a></li> | ||
| <li><a class="" href="software.html">Software</a></li> | ||
| <li><a class="selected" href="students.html">Students</a></li> | ||
| <li><a class="" href="essai.html">ESSAI'23-24</a></li> | ||
| <li><a class="" href="media.html">Media</a></li> | ||
| <!--li><a class="" href="quicklinks.html">Quick Links</a></li--> | ||
| </ul> | ||
| </nav> | ||
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| <div id="page"> | ||
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| <section> | ||
| <div class="container"> | ||
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| <p> | ||
| An overview of the students, current and former, whom I have supervised highlighting their projects and completions. | ||
| </p> | ||
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| <h2>Completions (Masters)</h2> | ||
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| <h3>Samuel Arthur Morris (2024)</h3> | ||
| <div class="citation"> | ||
| <p> | ||
| <span class="title">Policy Transfer for Deep Reinforcement Agents Using Game Entity Substitution - Applied to Infinite Mario</span> <br /> | ||
| School of Computing Technologies, STEM College, RMIT University, Melbourne, Australia. <br /> | ||
| <span class="supervisor">Supervisors: Dr. Timothy Wiley, Dr. Michael Dann.</span> | ||
| </p> | ||
| <p> | ||
| Abstract <br /> | ||
| Deep Reinforcement Learning (DRL) agents have shown impressive ability in | ||
| mastering computer games, but notoriously take a long time to learn. As an | ||
| agent progresses through a game, it will often encounter new states containing | ||
| previously unencountered game entities, e.g., new enemies. In such situations, | ||
| DRL agents typically struggle to generalise their prior knowledge to the new | ||
| entities, owing to differences in state and object representations. In particular, | ||
| even when new entities <em>behave</em> similarly to previously encountered ones, if they | ||
| <em>appear</em> to be different then DRL agents can take a long time to adapt. <br /> | ||
| Policy transfer learning offers a promising approach for allowing DRL | ||
| agents to adapt their knowledge; however, establishing the connection between | ||
| the newly presented states (the target task) and previously encountered ones | ||
| (the source task) requires guidance from a domain expert. Guidance in the | ||
| form of externally constructed mapping of state-action pairings, must be continually | ||
| maintained in response to new game entity encounters. <br /> | ||
| This thesis proposes an alternative approach, where policy transfer is accomplished | ||
| by leveraging an intermediate state transformation, removing the | ||
| need for manual mapping. Each entity is mapped to a unique entity ID, and | ||
| when a new game entity is encountered, a "substitution agent" strives to learn | ||
| a mapping between the new entity ID and a previously encountered one. For | ||
| example, if the new entity is a type of enemy, the substitution agent will ideally | ||
| learn to map the new ID to a previously encountered enemy’s ID, rather | ||
| than, say, the ID of a powerup item. Experimental results show that this | ||
| approach is effective, allowing for rapid improvement of end-of-episode scores | ||
| when encountering new entity representations in the game, <em>Infinite Mario</em>. | ||
| </p> | ||
| </div> | ||
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| <!-- <h2>Completions (Honours)</h2> --> | ||
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| </div> | ||
| </section> | ||
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| </div> | ||
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| <footer> | ||
| <div class="container"> | ||
| <time datestamp="2024-11">November 2024</time> | ||
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| </div> | ||
| </footer> | ||
| </body> | ||
| </html> | ||
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