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Spring 2020

Shafi, Hareem

Reinforcement learning (RL) has received wide attention in various fields lately. Model-free RL brings data-driven solutions that learn the control strategy directly from interaction with process data without the need for a process model. This is especially beneficial in the case of nonlinear...

Spring 2018

Travnik, Jaden

Recent advancements in reinforcement learning have made the field interesting to academia and industry alike. Many of these advancements depend on deep learning as a means to approximate a value function or a policy. This dependency usually relies on high performance hardware (e.g., a graphics...

Fall 2023

Zhang, Shida

Technological breakthroughs in renewable power generation, battery storage, electric mobility, and advanced data logistics are changing the electric grid. The huge influx of distributed energy resources (DERs), while important to curb carbon emissions, is not without consequences. The highly...

Spring 2022

Kumaraswamy, Raksha K

An important goal of online reinforcement learning algorithms is efficient data collection to learn near-optimal behaviour, that is, optimizing the exploration-exploitation trade-off to reduce the sample-complexity of learning. To improve sample-complexity of learning it is essential that the...

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 2018

Najafi, Saeed

Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do...

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 2019

Liu, Vincent

In this thesis, we investigate sparse representations in reinforcement learning. We begin by discussing catastrophic interference in reinforcement learning with function approximation, and empirically investigating difficulties of online reinforcement learning in both policy evaluation and...

2007

Wang, Tao, Schuurmans, Dale, Bowling, Michael, Lizotte, Daniel

Technical report TR07-05. We investigate novel, dual algorithms for dynamic programming and reinforcement learning, based on maintaining explicit representations of stationary distributions instead of value functions. In particular, we investigate the convergence properties of standard dynamic...

Spring 2016

Asadi Atui, Kavosh

Reinforcement learning algorithms are conventionally divided into two approaches: a model-based approach that builds a model of the environment and then computes a value function from the model, and a model-free approach that directly estimates the value function. The first contribution of this...