Guaranteed hierarchical reinforcement learning

Reinforcement learning (RL) is a sub-field of machine learning that aims to train an agent in an interactive environment to sequentially make choices via a process of trial-and-error, to maximize a total reward over time. RL has been studied for decades and has a strong and established theoretica...

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Main Author: Ang, Riley Xile
Other Authors: Arvind Easwaran
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
PAC
Online Access:https://hdl.handle.net/10356/175473
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1754732024-04-26T15:45:19Z Guaranteed hierarchical reinforcement learning Ang, Riley Xile Arvind Easwaran School of Computer Science and Engineering arvinde@ntu.edu.sg Computer and Information Science Reinforcement learning PAC Hierarchical reinforcement learning Q-learning Reinforcement learning (RL) is a sub-field of machine learning that aims to train an agent in an interactive environment to sequentially make choices via a process of trial-and-error, to maximize a total reward over time. RL has been studied for decades and has a strong and established theoretical foundation. Practically, it has gained prominence owing to projects in a wide range of fields including gaming, robotics, automation, etc. Despite its contributions and rise to popularity, RL is often resource-intensive in both its training time and memory requirements. Successfully training an agent with low margin of errors and high confidence bounds continues to remain an open research problem. Consequently, the focus of this project will be to use existing RL algorithms, particularly Speedy Q-Learning (SQL), a variant of tabular model-free Q-Learning, to design a Hierarchical Reinforcement Learning (HRL) agent in a continuous state space setting. Additionally, this project aims to evaluate the overall performance of the agent against proven theoretical bounds with the Probably Approximately Correct (PAC) framework. Bachelor's degree 2024-04-24T13:19:23Z 2024-04-24T13:19:23Z 2024 Final Year Project (FYP) Ang, R. X. (2024). Guaranteed hierarchical reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175473 https://hdl.handle.net/10356/175473 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Reinforcement learning
PAC
Hierarchical reinforcement learning
Q-learning
spellingShingle Computer and Information Science
Reinforcement learning
PAC
Hierarchical reinforcement learning
Q-learning
Ang, Riley Xile
Guaranteed hierarchical reinforcement learning
description Reinforcement learning (RL) is a sub-field of machine learning that aims to train an agent in an interactive environment to sequentially make choices via a process of trial-and-error, to maximize a total reward over time. RL has been studied for decades and has a strong and established theoretical foundation. Practically, it has gained prominence owing to projects in a wide range of fields including gaming, robotics, automation, etc. Despite its contributions and rise to popularity, RL is often resource-intensive in both its training time and memory requirements. Successfully training an agent with low margin of errors and high confidence bounds continues to remain an open research problem. Consequently, the focus of this project will be to use existing RL algorithms, particularly Speedy Q-Learning (SQL), a variant of tabular model-free Q-Learning, to design a Hierarchical Reinforcement Learning (HRL) agent in a continuous state space setting. Additionally, this project aims to evaluate the overall performance of the agent against proven theoretical bounds with the Probably Approximately Correct (PAC) framework.
author2 Arvind Easwaran
author_facet Arvind Easwaran
Ang, Riley Xile
format Final Year Project
author Ang, Riley Xile
author_sort Ang, Riley Xile
title Guaranteed hierarchical reinforcement learning
title_short Guaranteed hierarchical reinforcement learning
title_full Guaranteed hierarchical reinforcement learning
title_fullStr Guaranteed hierarchical reinforcement learning
title_full_unstemmed Guaranteed hierarchical reinforcement learning
title_sort guaranteed hierarchical reinforcement learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175473
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