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Skip to Search Results- 25White, Martha (Computing Science)
- 3White, Adam (Computing Science)
- 1Bowling, Michael (Computing Science)
- 1Farahmand, Amir-massoud (Computer Science, University of Toronto)
- 1Fyshe, Alona (Computing Science)
- 1Greiner, Russell (Computing Science)
- 12Reinforcement Learning
- 6Machine Learning
- 3Neural Networks
- 3Reinforcement learning
- 2Dyna
- 2Exploration
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Spring 2023
Gradient Descent algorithms suffer many problems when learning representations using fixed neural network architectures, such as reduced plasticity on non-stationary continual tasks and difficulty training sparse architectures from scratch. A common workaround is continuously adapting the neural...
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Fall 2021
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people listen. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound...
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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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Improving the reliability of reinforcement learning algorithms through biconjugate Bellman errors
DownloadSpring 2024
In this thesis, we seek to improve the reliability of reinforcement learning algorithms for nonlinear function approximation. Semi-gradient temporal difference (TD) update rules form the basis of most state-of-the-art value function learning systems despite clear counterexamples proving their...
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Spring 2020
Mapping the macrostructural connectivity of the living human brain is one of the primary goals of neuroscientists who study connectomics. The reconstruction of a brain's structural connectivity, aka its connectome, typically involves applying expert analysis to diffusion-weighted magnetic...
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Fall 2023
Partial observability---when the senses lack enough detail to make an optimal decision---is the reality of any decision making agent acting in the real world. While an agent could be made to make due with its available senses, taking advantage of the history of senses can provide more context and...
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Fall 2023
We study the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a critical step on the path to optimizing and automating water treatment. Before that, there are many questions to answer about predictability...
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Spring 2020
In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may still contain information that is useful for planning. In this thesis, we investigate the idea of using an imperfect...
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Fall 2020
For artificially intelligent learning systems to be deployed widely in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is challenging. Even finding approximately optimal joint policies of decentralized partially observable Markov...
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Strange springs in many dimensions: how parametric resonance can explain divergence under covariate shift.
DownloadFall 2021
Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on independently and identically ditributed (iid) data sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated inputs such as continual learning and reinforcement learning....