

Sam Devlin
Principal Researcher
About
My long term goal is to create autonomous agents capable of intelligible decision making in a wide range of complex environments with real world applications. In particular, my passion is using digital games to push boundaries in the capabilities of modern AI and making state of the art methods accessible to encourage the creation of new ways to play. To achieve these goals, my research currently focuses on machine learning, artificial intelligence, digital games and player experience.
Previously, I received an MEng degree in Computer Systems and Software Engineering from the University of York in 2009 including a year working with the human factors team at BAE Systems. After completing this degree I worked on traditional commercial game AI, integrating behaviour trees and nav mesh generation into the open-source game engine CrystalSpace as part of the Google Summer of Code program in 2009 and again in 2012. In 2013, I…
Featured content

Designer-centered reinforcement learning
In video games, nonplayer characters, bots, and other game agents help bring a digital world and its story to life. They can help make the mission of saving humanity feel urgent, transform every turn of a corner into a gamer’s…

Three new reinforcement learning methods aim to improve AI in gaming and beyond
Reinforcement learning (RL) provides exciting opportunities for game development, as highlighted in our recently announced Project Paidia—a research collaboration between our Game Intelligence group at Microsoft Research Cambridge and game developer Ninja Theory. In Project Paidia, we push the state…

Project Paidia: a Microsoft Research & Ninja Theory Collaboration
One goal of Project Paidia, a collaborative research project, is to drive state of the art research in reinforcement learning to enable game agents that learn to collaborate with human players.

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) competition is a new challenge that…