Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 210 for “"reinforcement learning (RL)"”.
-
Developing a generalized intelligent agent by processing information on webpages
… I designed and implemented a framework for reinforcement learning (RL) agents to interact with a web environment. With this framework, I introduce a new challenge for RL agents to learn human activity on the web. By defining a series of tasks such as using the web as a navigable resource to …
-
Deploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apollo
Deep reinforcement learning (RL) has gained increasing popularity as an approach to achieving dynamic behaviors on legged robots. However, transferring RL behaviors from simulation to reality is a challenging process: imperfect sensors, simulation models, control architecture, and latency all …
-
Addressing stale gradients in asynchronous federated deep reinforcement learning
Advancements in reinforcement learning (RL) via deep neural networks have enabled their application to a variety of real-world problems. However, these applications often suffer from long training times. While attempts to distribute training have been successful in controlled scenarios, they face …
-
Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents
… of state-of-the art offline and model-based reinforcement learning (RL) algorithms deteriorates significantly when subjected to severe data scarcity and the presence of heterogeneous agents. In this work, we propose a model-based offline RL method to approach this setting. Using all available …
-
An Introduction to Reinforcement Learning
This thesis presents a new course textbook on reinforcement learning (RL) with a focus on algorithms and their properties. The textbook is suitable for a one-semester introductory undergraduate course on RL for students with prior experience in basic probability, linear algebra, and multivariable …
-
The Limits of Temporal Abstractions for Reinforcement Learning with Sparse Rewards
… abstractions that are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an environment that make skills useful, there has been little theoretical work aimed to characterize these properties precisely. This work …
-
Optimizing Priority-Based Search for Lifelong Multi-Agent Path Finding
… scenarios. This work explores how learning-based methods can improve PBS decision-making. We develop supervised learning (SL) policies trained from high-quality beam search trajectories and reinforcement learning (RL) policies learned directly through simulation, enabling adaptive …
-
Learning from Experience: An Interactive and Ethical Curriculum for Teaching Reinforcement Learning
… literacy are also increasingly important fields. Reinforcement learning (RL) plays and will continue to play an intense role in many systems, including online advertising, self-driving cars, and personalized tutoring. There is a corresponding need for education of RL. In my thesis work, I …
-
Inverse Reinforcement Learning and Routing Metric Discovery
… thesis presents a method for utilizing inverse reinforcement learning (IRL)techniques for the purpose of discovering a composite metric used by a dynamic routing algorithm on an Internet Protocol (IP) network. The network and routing algorithm are modeled as a reinforcement learning (RL) agent …
-
Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation
… framework that facilitates rapid prototyping of reinforcement learning (RL) and an automatic feature engineering method are proposed. Additionally, RL techniques are implemented to a digital twin of Chattanooga smart corridor. Regarding transit simulations, a toolkit for calibrating large-scale …
-
Team Learning from Human Demonstration with Coordination Confidence
… an array of techniques proposed to speed-up reinforcement learning (RL), learn- ing from human demonstration has a proven record of success. A related technique, called Human Agent Transfer (HAT), and its confidence-based derivatives have been successfully applied to single agent RL. This …
-
Steps towards proof construction using reinforcement learning : environments and models for hypothesis-posing as subtask creation
Despite recent advances in reinforcement learning (RL) that have allowed AI algorithms to master games such as Go from scratch, scant progress has been made on applying RL to one of the first tasks seen as susceptible to automation: theorem proving. I present steps towards training agents to …
-
Market making in dry waters : reinforcement learning strategies for market making in illiquid markets
This thesis explores the application of reinforcement learning (RL) strategies to market making in illiquid markets. Traditional market making approaches often rely on static, rule-based strategies, which can struggle in illiquid environments. The study implements three RL algorithms: Deep …
-
ARBITRAGE STRATEGIES IN PERPETUAL FUTURES AND STOCK INDEX FUTURES
… arbitrage in perpetual futures, which track underlying prices through a funding swap mechanism. We show that the clamping function embedded in the mechanism—previously overlooked in the literature—creates inherent no-arbitrage bounds that persist even in the absence of transaction fees. Using two …
-
Efficient Reinforcement Learning for Control
… has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a …
-
Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
… that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures …
-
An application of stochastic dynamic programming to group revenue management
… tool used in large industries, especially by airline companies. This tool aims at optimising revenues by a better control of inventory and pricing among other factors. In this thesis, a stochastic optimality control problem which consists of finding an optimal policy to when it is profitable (or …
-
AstroBug: automatic game bug detection using deep learning
… further enhanced the framework by implementing Reinforcement Learning (RL) agent to autonomously gather datasets, effectively addressing the need for human players to collect data and manually browse through games. The enhancement was performed on a Role-Playing Game (RPG). The outcomes obtained …
-
Quasimetric decision transformer: enhancing goal-conditioned reinforcement learning with structured distance guidance
Recent works have shown that tackling offline Reinforcement Learning (RL) with a conditional policy produces promising results. Decision Transformer (DT) have shown promising results in offline RL by leveraging sequence modeling. However, standard DTs rely on Returns-to-Go (RTG) tokens, which are …
-
TCP congestion control using reinforcement learning
… of Internet connectivity, with 85% of the worlds Internet traffic being TCP based. TCP however, is slow to adapt to changes in the network, drastically reducing the throughput at the first sign of possible congestion, thereby preventing rapid restoration of the throughput. Mitigating this …
Page 1 of 11