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Skip to Search Results- 3Machine Learning
- 2Artificial Intelligence
- 1Emergent Communication
- 1Monte Carlo
- 1Multi-Agent Reinforcement Learning
- 1Multi-armed Bandits
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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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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 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...