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Skip to Search Results- 25White, Martha (Computing Science)
- 21Bowling, Michael (Computing Science)
- 3Schuurmans, Dale (Computing Science)
- 3White, Adam (Computing Science)
- 1Bellemare, Marc (Google Brain)
- 1Farahmand, Amir-massoud (Computer Science, University of Toronto)
- 17Reinforcement Learning
- 10Machine Learning
- 7Artificial Intelligence
- 4Machine learning
- 4Reinforcement learning
- 3Exploration
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Fall 2023
The problem of missing data is omnipresent in a wide range of real-world datasets. When learning and predicting on this data with neural networks, the typical strategy is to fill-in or complete these missing values in the dataset, called impute-then-regress. Much less common is to attempt to...
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Spring 2019
In this thesis we introduce a new loss for regression, the Histogram Loss. There is some evidence that, in the problem of sequential decision making, estimating the full distribution of return offers a considerable gain in performance, even though only the mean of that distribution is used in...
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Fall 2019
Artificial agents have been shown to learn to communicate when needed to complete a cooperative task. Some level of language structure (e.g., compositionality) has been found in the learned communication protocols. This observed structure is often the result of specific environmental pressures...
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Spring 2019
In the reinforcement learning (RL) problem an agent must learn how to act optimally through trial-and-error interactions with a complex, unknown, stochastic environment. The actions taken by the agent influence not just the immediate reward it observes but also the future states and rewards it...
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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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Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
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In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
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Fall 2022
This thesis investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives,...
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Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
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Policy gradient methods typically estimate both explicit policy and value functions. The long-extant view of policy gradient methods as approximate policy iteration---alternating between policy evaluation and policy improvement by greedification---is a helpful framework to elucidate algorithmic...
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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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Hindsight Rational Learning for Sequential Decision-Making: Foundations and Experimental Applications
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This thesis develops foundations for the development of dependable, scalable reinforcement learning algorithms with strong connections to game theory. I present a version of rationality for learning---one grounded in the learner's experience and connected with the rationality concepts of...