Policy Adaptation over Environmental Configurations in Survivor Type Games
This thesis explores the applicability and effectiveness of direct policy transfer in reinforcement learning (RL), with a focus on environmental configurations within the open-source game openSURVIVORS. We investigate how policy transfer can decrease the time to threshold and enhance jumpstart and a...
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2023
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Online Access: | https://hdl.handle.net/11250/3099258 |
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/3099258 2023-11-12T04:16:50+01:00 Policy Adaptation over Environmental Configurations in Survivor Type Games Markus Johannes Pedersen Ole Christian Eidheim Jonathan Jørgensen 2023 application/pdf https://hdl.handle.net/11250/3099258 eng eng NTNU no.ntnu:inspera:145904930:35331123 https://hdl.handle.net/11250/3099258 Master thesis 2023 ftntnutrondheimi 2023-11-01T23:46:49Z This thesis explores the applicability and effectiveness of direct policy transfer in reinforcement learning (RL), with a focus on environmental configurations within the open-source game openSURVIVORS. We investigate how policy transfer can decrease the time to threshold and enhance jumpstart and asymptotic performance across various environmental configurations. Our experimental results highlight the strong influence of the game's unique environmental dynamics on policy effectiveness and transferability, with different RL agents exhibiting varied performance based on their respective training environments. While the asymptotic performance of agents trained via policy transfer remains inconclusive due to non-convergence within the study's scope, our findings demonstrate that policy transfer outperforms training from scratch, indicating its potential advantages in learning new environments. This research advances the understanding of policy transfer in RL, offering insights into training agents more effectively across diverse environments. Denne oppgaven utforsker effektiviteten til direkte overføring av evenene til i reinforcement learning agent, med fokus på problems konfigurasjoner innenfor det åpne kildekode-spillet openSURVIVORS. Vi undersøker hvordan overføring av agentenes evner kan redusere trenings tiden på tvers av ulike problem konfigurasjoner. Våre eksperimentelle resultater fremhever den sterke påvirkningen av spillets dynamikk på evnene til agentene på tvers av problemkonfigurasjoner. RL-agentene viser varierende ytelse basert på deres respektive treningsmiljøer. Våre funn viser at at overføring av evner over konfigurasjoner presterer bedre enn trening fra bunnen av, noe som indikerer potensielle fordeler ved denne metoden. Denne forskningen bidrar til å øke forståelsen av overføring av evner i RL og gir innsikt i mer effektiv trening av agenter på tvers av ulike miljøer. Master Thesis evenene NTNU Open Archive (Norwegian University of Science and Technology) |
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Open Polar |
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NTNU Open Archive (Norwegian University of Science and Technology) |
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ftntnutrondheimi |
language |
English |
description |
This thesis explores the applicability and effectiveness of direct policy transfer in reinforcement learning (RL), with a focus on environmental configurations within the open-source game openSURVIVORS. We investigate how policy transfer can decrease the time to threshold and enhance jumpstart and asymptotic performance across various environmental configurations. Our experimental results highlight the strong influence of the game's unique environmental dynamics on policy effectiveness and transferability, with different RL agents exhibiting varied performance based on their respective training environments. While the asymptotic performance of agents trained via policy transfer remains inconclusive due to non-convergence within the study's scope, our findings demonstrate that policy transfer outperforms training from scratch, indicating its potential advantages in learning new environments. This research advances the understanding of policy transfer in RL, offering insights into training agents more effectively across diverse environments. Denne oppgaven utforsker effektiviteten til direkte overføring av evenene til i reinforcement learning agent, med fokus på problems konfigurasjoner innenfor det åpne kildekode-spillet openSURVIVORS. Vi undersøker hvordan overføring av agentenes evner kan redusere trenings tiden på tvers av ulike problem konfigurasjoner. Våre eksperimentelle resultater fremhever den sterke påvirkningen av spillets dynamikk på evnene til agentene på tvers av problemkonfigurasjoner. RL-agentene viser varierende ytelse basert på deres respektive treningsmiljøer. Våre funn viser at at overføring av evner over konfigurasjoner presterer bedre enn trening fra bunnen av, noe som indikerer potensielle fordeler ved denne metoden. Denne forskningen bidrar til å øke forståelsen av overføring av evner i RL og gir innsikt i mer effektiv trening av agenter på tvers av ulike miljøer. |
author2 |
Ole Christian Eidheim Jonathan Jørgensen |
format |
Master Thesis |
author |
Markus Johannes Pedersen |
spellingShingle |
Markus Johannes Pedersen Policy Adaptation over Environmental Configurations in Survivor Type Games |
author_facet |
Markus Johannes Pedersen |
author_sort |
Markus Johannes Pedersen |
title |
Policy Adaptation over Environmental Configurations in Survivor Type Games |
title_short |
Policy Adaptation over Environmental Configurations in Survivor Type Games |
title_full |
Policy Adaptation over Environmental Configurations in Survivor Type Games |
title_fullStr |
Policy Adaptation over Environmental Configurations in Survivor Type Games |
title_full_unstemmed |
Policy Adaptation over Environmental Configurations in Survivor Type Games |
title_sort |
policy adaptation over environmental configurations in survivor type games |
publisher |
NTNU |
publishDate |
2023 |
url |
https://hdl.handle.net/11250/3099258 |
genre |
evenene |
genre_facet |
evenene |
op_relation |
no.ntnu:inspera:145904930:35331123 https://hdl.handle.net/11250/3099258 |
_version_ |
1782333894087933952 |