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Tsuruoka Lab
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The lab studies reinforcement learning and related topics, with game AI and natural language processing as its two main pillars.
AboutAbout the lab
Handling games and language with computers
The Tsuruoka Lab centers its research on two themes: game AI and natural language processing.
Its research covers intelligent information processing by computers, including reinforcement learning.
Each member pursues research according to their own interests.
The lab brings together people from Japan and many other countries.
Key terms
- Natural language processing
- Technology for handling the languages people use in everyday life, such as Japanese and English, with computers
- Reinforcement learning
- A machine learning method in which the results of trial and error are given a "reward," so that the system learns better actions on its own
- Imperfect-information game
- A game in which some information is hidden from the players, such as an opponent's hand. Examples include poker and mahjong
ResearchResearch themes
The lab studies intelligent information processing by computers, centered on fields such as natural language processing, game AI, and reinforcement learning. Its work ranges from developing new algorithms and deep learning methods to building information processing systems that can be used in practice.
*Illustrative imageEquilibrium Strategies in Card Games
This research uses the card game Hearthstone to compute equilibrium strategies in imperfect-information games, where some information is hidden from the players. Using a method called CFR, it addresses both deck construction and how to choose actions during a match.
*Illustrative imageMultilingual Natural Language Processing
This research aims to enable a single model to handle multiple languages at the same time. The lab develops models that take advantage of what languages have in common, and analyzes multilingual models.
*Illustrative imageSentence Generation with Reinforcement Learning
Evaluating sentences only by word-by-word matching cannot handle paraphrases or differences in how a sentence is structured. By using a flexible criterion, such as semantic closeness to the reference sentence, as the reward, the lab trains models to generate sentences through reinforcement learning.
MembersFaculty
The members of the Tsuruoka Lab consist of faculty members, doctoral, master's, and undergraduate students, and research students.As of 2026
- ProfessorYoshimasa TsuruokaPublications ↗
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Othello-Standard-Board” Zen ar218 / Public domain (cropped)
- Equilibrium Strategies in Card Games: “Mahjong Tiles ( )” Lian Jiayuan / CC0 (cropped)
- Multilingual Natural Language Processing: “Rosetta Stone” Hans Hillewaert / CC BY-SA 4.0 (cropped)
- Sentence Generation with Reinforcement Learning: “Casse de typographe, Grandjean - musée Champollion” VIGNERON / CC BY 3.0 (cropped)
- Related lab (Iba Lab): “The flock of starlings acting as a swarm. - geograph.org.uk - 124593” John Holmes / CC BY-SA 2.0 (cropped)
- Related lab (Yamasaki Lab): “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Related lab (Hirose-Natsuaki Lab): “Radar image of the Danube Delta ESA223752” European Space Agency / CC BY-SA 3.0 igo (cropped)
- Related lab (Irie-Kadomoto Lab): “12-inch silicon wafer” Peellden / CC BY-SA 3.0 (cropped)
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