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Fall 2012

Mazrooei, Parisa

Collusion is the deliberate cooperation of two or more parties to the detriment of others. While this behaviour can be highly profitable for colluders (for example, in auctions and online games), it is considered illegal and unfair in many sequential decision-making domains and presents many...

Spring 2010

Naddaf, Yavar

This research focuses on developing AI agents that play arbitrary Atari 2600 console games without having any game-specific assumptions or prior knowledge. Two main approaches are considered: reinforcement learning based methods and search based methods. The RL-based methods use feature vectors...

Spring 2013

Lanctot, Marc

In this thesis, we investigate the problem of decision-making in large two-player zero-sum games using Monte Carlo sampling and regret minimization methods. We demonstrate four major contributions. The first is Monte Carlo Counterfactual Regret Minimization (MCCFR): a generic family of...

Spring 2015

Sriram, Srinivasan

In this thesis, I study the problem of Monte-Carlo Planning in deterministic do- mains with sparse rewards. A popular algorithm in this suite, UCT, is studied. A new algorithm to incorporate state generalization in UCT using estimates of sim- ilar nodes and a distance metric is presented. The...

Fall 2018

Holland, Gordon Z.

Dyna is an architecture for reinforcement learning agents that interleaves planning, acting, and learning in an online setting. This architecture aims to make fuller use of limited experience to achieve better performance with fewer environmental interactions. Dyna has been well studied in...