Alisa
Devlic

Profile

Alisa obtained her Ph.D. from the Royal Institute of Technology (KTH), Stockholm, Sweden and did her postdoctoral work at IMT Atlantique, Rennes, France. During her career, she worked both in industry research labs (Ericsson Research and Huawei Technologies) and academia (KTH, IMT Atlantique, and University of Zagreb). Alisa (co)authored more than 30 papers and obtained best paper awards at the following venues: IEEE WoWMoM 2015, IEEE ICC 2017 and IEEE MMSP 2018. She also received a scholarship from Swedish Institute for a part of her Ph.D. studies and was awarded several undergraduate rewards from University of Zagreb for being one of the best students in the generation.

Message

“I work as a senior research scientist at Sony AI focusing on applying and advancing the state-of-the-art in deep reinforcement learning in the gaming domain. My current research focus is on inventing new methods that can explain and improve the agent's learning performance and reduce the AI designer's engineering efforts to develop such a system. My long-term goal is to create autonomous agents capable of quickly learning and adapting to new tasks in complex environments that can engage in new ways of gameplay and improve player experiences.”

Publications

Value Function Decomposition for Iterative Design of Reinforcement Learning Agents

NeurIPS, 2022
James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone

Designing reinforcement learning (RL) agents is typically a difficult process that requires numerous design iterations. Learning can fail for a multitude of reasons and standard RL methods provide too few tools to provide insight into the exact cause. In this paper, we show …

Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

Nature, 2022
Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, Hao Chih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead Amago, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block…

Blog

January 11, 2024 | GT Sophy | Game AI

From Hypothesis to Reality: The GT Sophy Team Explains the Evolution of the Breakthrough AI Agent

Since its inception in 2020, Sony AI has been committed to enhancing human imagination and creativity through the acceleration of AI research and development. One of the first examples of this work can be found within the organiza…

Since its inception in 2020, Sony AI has been committed to enhancing human imagination and creativity through the acceleration of …

September 15, 2022 | GT Sophy | Game AI

Don’t Cross That Line! How Our AI Agent Learned Sportsmanship

GT SOPHY TECHNICAL SERIES Starting in 2020, the research and engineering team at Sony AI set out to do something that had never been done before: create an AI agent that could beat the best drivers in the world at the PlayStation®…

GT SOPHY TECHNICAL SERIES Starting in 2020, the research and engineering team at Sony AI set out to do something that had never be…

April 4, 2022 | Life at Sony AI

Meet the Team #4: Kenta, Alisa and Thomas

The next installments of our Meet the Team series will feature members of the global Sony AI team who contributed to thegroundbreaking research, Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning, and created…

The next installments of our Meet the Team series will feature members of the global Sony AI team who contributed to thegroundbrea…

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