Member

Daiko Kishikawa

Estimation of intension via deep inverse reinforcement learning

Dan Zhou	Evolutionary Computation for Multi-objective Reinforcement Learning
Du Jiqing	Adaptive Control System via Deep Reinforcement Learning

Takumi Saiki

Traffic signal control via multi-objective reinforcement learning

Wataru Mogi

Mechanism Design: Realization of Traffic Equilibrium Allocation via Inverse Reinforcement Learning

Akinori Tamura	Multi-objective reinforcement learning for nonconvex Pareto front estimation
Ryota Toriumi	Integrated Reinforcement Learning and Control Theory for Automatic Railway Operation
Yuki Mori	Improving scalability of multi-agent deep reinforcement learning
Yuta Mori	Cooperative Measurements in Multi-Agent Deep Reinforcement Learning

Takeshi Kunieda	Identification of good structure/bad structure situations for optimal hazard avoidance control
Masaharu Saito	Identification of multiple intentions latent in heterogeneous multi-agent trajectories
Kentaro Sakai	Off-line Reinforcement Learning for Acquiring Robust Control Strategies
Keiju Tazawa	Estimation of preference order for behavior change

Yuta Ono	Utility Estimation Based on Action History Using Decision Transformer
Yuta Tokuhiro	Consideration of ChatGPT and human tagging characteristics of FAQs for realization of automatic response system
Hayato Chujo	Realization of environmental protection and comfort by introducing event-driven control
Takuya Maniwa	Generation of optimal measures based on multiple suboptimal measures
Shota Shinohara	Robustness Verification of Automatic Railway Control by Reinforcement Learning
Kouta Minoshima	Imitation Learning from Small Data
Misato Kobayashi	Automated Vehicle Formation Driving: Using Attention to Maintain Formation
Kento Nagata	System Identification and Design Methods by Introducing Model-Free Reinforcement Learning: ~Complementary Use of Control Theory~
Seitetsu Kumataki	DRRL (PID control + deep reinforcement learning) stability improvement by Human Feedback
Yusuke Yasui	Introduction of Reinforcement Learning in Horizon Decision Making for Model Predictive Control: ~Complementary Use of Control Theory~