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Skip to Search Results- 35Reinforcement learning
- 5Machine learning
- 3Artificial Intelligence
- 3Non-player character
- 2Adaptive switching
- 2Intelligent agents
- 2Cutumisu, Maria
- 1Al-Saffar, Mohammed
- 1Atrazhev, Peter
- 1Bastani, Meysam
- 1Bowling, Michael
- 1Carbonaro, Mike
- 30Graduate and Postdoctoral Studies (GPS), Faculty of
- 30Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 3Computing Science, Department of
- 3Computing Science, Department of/Technical Reports (Computing Science)
- 1Electrical and Computer Engineering, Department of
- 1Electrical and Computer Engineering, Department of/Journal Articles (Electrical and Computer Engineering)
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Fall 2013
This thesis presents new algorithms for dealing with large scale reinforcement learning problems. Central to this work is the Atari 2600 platform, which acts as both a rich evaluation framework and a source of challenges for existing reinforcement learning methods. Three contributions are...
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Fall 2010
Non-Player Character (NPC) behaviors in today’s computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial...
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2006
Cutumisu, Maria, Szafron, Duane, Roy, Thomas, Carbonaro, Mike, McNaughton, Matthew, Schaeffer, Jonathan, Onuczko, Curtis
Many computer games use custom scripts to control the ambient behaviors of non-player characters (NPCs). Therefore, a story writer must write fragments of computer code for the hundreds or thousands of NPCs in the game world. The challenge is to create entertaining and non-repetitive behaviors...
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Fall 2021
A common scientific challenge for putting a reinforcement learning agent into practice is how to improve sample efficiency as much as possible with limited computational or memory resources. Such available physical resources may vary in different applications. My thesis introduces some approaches...
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Fall 2017
Model-free off-policy temporal-difference (TD) algorithms form a powerful component of scalable predictive knowledge representation due to their ability to learn numerous counter- factual predictions in a computationally scalable manner. In this dissertation, we address and overcome two...
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Spring 2023
The overall goal of this work was to design an intelligent method to reduce the cognitive and physical burdens associated with walking using lower-limb exoskeletons after paralysis. Lower-limb exoskeletons with many operating modes (i.e., walking patterns) can be challenging to work with....
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...
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Spring 2014
Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin...
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Spring 2024
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing intrinsic...