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.
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Showing 1 to 20 of 102 for “"Q-learning"”.
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Continuous state Q-learning
Q-learning is a solution technique developed to solve classical Markov Decision Processes, MDPs. Markov Decision Processes are models for sequential decision making problems and address many classical control problems. In Chapter I, this paper discusses the model and some standard solution …
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Modifications of Q-learning to Optimize Dynamic Treatment Regimes
… DTR for an individual subject, among which Q-learning with linear regression is widely used due to its simplicity and ease of interpretation. This thesis discusses three important challenges that cause problems in the implementation of Q-learning and proposes multiple modifications of …
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Non-Reciprocating Sharing Methods in Cooperative Q-Learning Environments
… simulation with cooperative reinforcement learning (RL) for homogeneous agents focuses on developing sharing strategies that are adopted and used by all agents in the environment. These sharing strategies are considered to be reciprocating because all participating agents have a predefined …
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Active heterogeneous graph neural networks with per-step meta-q-learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Stochastic approximation for non-expansive maps : application to Q-learning algorithms
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Solving the reader collision problem with a hierarchical Q-learning algorithm
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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Predicting Mutational Pathways of Influenza A H1N1 Virus using Q-learning
… metric where available. Applying reinforcement learning with the vertices as states, edges as actions, and fitness as the reward, we learn the high likelihood mutational pathways and optimal policy, without exploring the entire space of the graph, $G$. Our average predicted versus actual …
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The development of a robotic test bed with applications in Q-learning
… an autonomous ground vehicle for experiments in learning and intelligent transportation research. We then implement the Q-Learning algorithm to teach the robot to navigate towards a light source. The vehicle platform is based on the Tamiya TXT-1 chassis which is out\ufb01tted with an onboard …
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Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy
… thesis the author investigated the use of a Q-learning based path planning algorithm to investigate how effective it is in saving energy. It is important to pursue any means to save energy in this day and age, due to the excessive exploitation of natural resources and in order to prevent drops …
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A Distributed Q-learning Classifier System for task decomposition in real robot learning problems
A distributed reinforcement-learning system is designed and implemented on a mobile robot for the study of complex task decomposition in real robot learning environments. The Distributed Q-learning Classifier System (DQLCS) is evolved from the standard Learning Classifier System (LCS) proposed by …
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Learning to Coordinate Efficiently through Multiagent Soft Q-Learning in the presence of Game-Theoretic Pathologies
… investigate the convergence of multiagent soft Q-learning in continuous games where learning is most likely to be affected by relative overgeneralisation. While this will occur more often in multiagent independent learner problems, it is present in joint-learner problems when information is not …
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Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations
… for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art …
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Reinforcement learning based control for arrays of point absorber wave energy converters
… of values can be achieved using reinforcement learning algorithms, such as Q-learning. Performance of the control system was evaluated through simulation of the point absorber. For regular waves, performance could be gauged against an analytical solution, and for irregular waves a value for …
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Path planning and control of flying robots with account of human’s safety perception
… the changes in the environment. A HMM-based Q-learning is considered for computing the online optimal policy. The HMM-based Q-learning estimates the hidden state of the human in interactions with the robot. The state estimator in the HMM-based Q-learning infers the hidden states of the human …
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Reinforcement learning for power scheduling in a grid-tied pv-battery electric vehicles charging station
… investigates the application of reinforcement learning (RL) techniques in solving the power scheduling problem in a grid-tied PV-powered EV charging station with the incorporation of a battery energy storage system. RL is a reward-motivated optimization technique that was derived from the way …
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Game theory with learning for cyber security monitoring
… with high false positive rates. While machine learning and game theoretic models have been applied for intrusion detection, machine learning techniques lack the ability to model the rationality of the players, while the game theoretic approaches rely on the strict assumption of full rationality …
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