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Skip to Search Results- 30Reinforcement learning
- 4Machine learning
- 2Adaptive switching
- 2Artificial Intelligence
- 2Non-player character
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Spring 2012
Mirian HosseinAbadi, MahdiehSadat
In this thesis we propose a computational model of animal behavior in spatial navigation, based on reinforcement learning ideas. In the field of computer science and specifically artificial intelligence, replay refers to retrieving and reprocessing the experiences that are stored in an abstract...
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Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning
DownloadFall 2016
Powered prosthetic arms with numerous controllable functions (i.e., grip patterns or movable joints) can be challenging to operate. Gated control---a common control method for myoelectric arms and other human-machine interfaces---allows users to select a function by switching through a static...
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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Fall 2009
Most story-based games today have manually-scripted non-player characters (NPCs) and the scripts are usually simple and repetitive since it is time-consuming for game developers to script each character individually. ScriptEase, a publicly-available author-oriented developer tool, attempts to...
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Fall 2021
An oft-ignored challenge of real-world reinforcement learning is that, unlike standard simulated environments, the real world does not pause when agents make learning updates. As standard simulated environments do not address this real-time aspect of learning, most available implementations of...
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Spring 2018
Facial expressions and other body language are important for human commu- nication. They complement speech and make the process of communication simple and sustainable. However, the process of communication using existing approaches to human-machine interaction is not intuitive as that of human...
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Fall 2015
Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence. The quantity of knowledge, or knowing a lot, may be nicely thought of as making and updat- ing many predictions about...
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Fall 2023
Language models are a fundamental component of natural language processing (NLP) systems. Numerous successful deployments of modern artificial intelligence systems are based on language models, including GPT-4 and ChatGPT. In practice, they are often trained with the teacher forcing objective,...
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Fall 2020
This dissertation investigates the properties of representations learned by modern deep reinforcement learning systems. Representation learning plays an important roll in reinforcement learning. A representation contains information extracted from states---the description of the current situation...
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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...