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Skip to Search Results- 30White, Martha (Computing Science)
- 21Bowling, Michael (Computing Science)
- 5White, Adam (Computing Science)
- 3Schuurmans, Dale (Computing Science)
- 1Bellemare, Marc (Google Brain)
- 1Cutkosky, Ashok (Electrical and Computer Engineering)
- 18Reinforcement Learning
- 11Machine Learning
- 7Artificial Intelligence
- 4Machine learning
- 4Reinforcement learning
- 3Exploration
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Spring 2022
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this work, focusing on fixed design linear regression with Gaussian noise and a...
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Spring 2010
In this work, we present a unified, general approach to variance reduction in agent evaluation using machine learning to minimize variance. Evaluating an agent's performance in a stochastic setting is necessary for agent development, scientific evaluation, and competitions. Traditionally,...
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Fall 2009
For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies....
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Fall 2024
Most work in online reinforcement learning (RL) tunes hyperparameters in an offline phase without accounting for the said interaction. This empirical methodology is a reasonable approach to assess how well algorithms can perform but is limited when evaluating algorithms for practical deployment...
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Fall 2024
Over the last decade, machine learning (ML) has lead to advances in many fields, such as computer vision, online decision-making, robotics, natural language processing, and many others. The algorithms driving these successes typically have one or more user-specified free variables called...
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Spring 2016
Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...
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Spring 2015
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that do not learn a value function and instead directly represent policy, often need fewer parameters to learn good policies....
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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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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Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...