Takuma joined Sony AI in Oct 2020 as a machine learning engineer, following part-time research positions at Sony R&D Center, Ghelia and Okinawa Institute of Science and Technology. He received his master’s degree in computer science at Keio University in 2019, and is currently pursuing his Ph.D. Takuma’s main research interest is deep reinforcement learning. He developed an offline deep reinforcement learning library, d3rlpy, funded by the IPA MITOU program in 2020, and was certified as a MITOU Super Creator in 2021.


“I am currently working with the Game AI flagship project where we are tackling many practical and theoretical reinforcement learning challenges. There are an enormous number of potential projects where we can leverage the power of reinforcement learning at Sony, and I'm very excited to see what we can do for Sony and the future.”


Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

Nature, 2022
Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. 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…

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

NeurIPS, 2021
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ…

d3rlpy: An Offline Deep Reinforcement Learning Library

NeurIPS, 2021
Takuma Seno, Michita Imai*

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a number of offline deep RL algorithms as well as online algorithms via a user-friendly API. To assist deep RL research and development projects, …


June 14, 2022 | Gaming | GT Sophy

Training the World’s Fastest Gran Turismo Racer

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


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