19 citations · 69 across the 18 of their papers we have counts for
5 papers · 1 filter
Optimistic Proximal Policy Optimization
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains. However, it is known that…
Building a Computer Mahjong Player via Deep Convolutional Neural Networks
Shiqi Gao, Fuminori Okuya, Yoshihiro Kawahara +1
The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great ac…
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori +2
Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options t…
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning
Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, distur…
Neural Fictitious Self-Play on ELF Mini-RTS
Keigo Kawamura, Yoshimasa Tsuruoka
Despite the notable successes in video games such as Atari 2600, current AI is yet to defeat human champions in the domain of real-time strategy (RTS) games. One of the reasons is…