Application of reinforcement learning methods to computer game dynamics
type: CreativeWork
description: The dynamics of the game world present both challenges and opportunities for AI to make a useful difference. Learning smart behaviours for game assets is a first step towards realistic conflict or cooperation. The scope this thesis is the application of Reinforcement Learning to moving assets in the game world. Game sessions a generate stream data on asset's performance which must be processed on the fly. The lead objective is to produce fast, lightweight and flexible learning algorithms for run-time embedding. The motivation from current work is to shorten the time to achieve a workable policy solution by investigating the exploration / exploitation balance, overcome the curse of dimensionality of complex systems, and avoid the use of extra endogenous parameters which require multiple data passes and use a simple state aggregation rather than functional approximation. How action selection (AS) contributes to efficient learning is a key issue in RL since is determines the balance between exploiting and confirming the current policy or exploring an early less likely policy which may prove better in the long run. The methodology deploys the simulation of several AS using 10-armed bandit problem averaged over 10000 epochs. The results show a considerable variation in performance in terms of latency and asymptotic direction. The Upper Confidence Bound comes out leader over most of the episode range, especially at about 100. Using insight from action selection order statistics are applied to determine a criterion for the convergence of policy evaluation. The probability that the action of maximum sample mean is indeed the action of maximum population mean (PMSMMPM) is calculated using the 3 armed bandit problem. PMSMMPM reaches 0.988 by play 26 which provides evidence for it as a convergence criterion. An iteration stopping rule is defined using PMSMMPM and it shows plausible properties as the population parameters are varied. A mathematical analysis of the approximation…
abstract: The dynamics of the game world present both challenges and opportunities for AI to make a useful difference. Learning smart behaviours for game assets is a first step towards realistic conflict or cooperation. The scope this thesis is the application of Reinforcement Learning to moving assets in the game world. Game sessions a generate stream data on asset's performance which must be processed on the fly. The lead objective is to produce fast, lightweight and flexible learning algorithms for run-time embedding. The motivation from current work is to shorten the time to achieve a workable policy solution by investigating the exploration / exploitation balance, overcome the curse of dimensionality of complex systems, and avoid the use of extra endogenous parameters which require multiple data passes and use a simple state aggregation rather than functional approximation. How action selection (AS) contributes to efficient learning is a key issue in RL since is determines the balance between exploiting and confirming the current policy or exploring an early less likely policy which may prove better in the long run. The methodology deploys the simulation of several AS using 10-armed bandit problem averaged over 10000 epochs. The results show a considerable variation in performance in terms of latency and asymptotic direction. The Upper Confidence Bound comes out leader over most of the episode range, especially at about 100. Using insight from action selection order statistics are applied to determine a criterion for the convergence of policy evaluation. The probability that the action of maximum sample mean is indeed the action of maximum population mean (PMSMMPM) is calculated using the 3 armed bandit problem. PMSMMPM reaches 0.988 by play 26 which provides evidence for it as a convergence criterion. An iteration stopping rule is defined using PMSMMPM and it shows plausible properties as the population parameters are varied. A mathematical analysis of the approximation…
author: Booth, David John
keywords: 790 Recreational & performing arts
date published: 2018-01
date modified: 2026-07-24T02:54:33Z
provider: London Metropolitan University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: London Metropolitan University