Search

Spring 2020

Yasui, Nikolaus Winget

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...

Spring 2020

Jafferjee, Taher

In Dyna style algorithms, reinforcement learning (RL) agents use a model of the environment to generate simulated experience. By updating on this simulated experience, Dyna style algorithms allow agents to potentially learn control policies in fewer environment interactions than agents that use...

Spring 2019

Imani, Ehsan

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...

Fall 2020

Chan, Alan

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...

Fall 2021

Hashemzadeh, Maryam

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...

Fall 2021

Pan, Yangchen

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...

Spring 2020

Farzane Aminmansour

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...

Spring 2020

Zaheer, Muhammad

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...

Fall 2020

Sokota, Samuel

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...

Fall 2021

Banman, Kirby

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....