1 citations · 1 across the 2 of their papers we have counts for
4 papers
Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning
Guanquan Wang, Yoshimasa Tsuruoka
Diffusion-based trajectory planners have shown strong performance in offline reinforcement learning, but their iterative denoising process often incurs high inference cost. Consist…
Consistency Trajectory Planning: High-Quality and Efficient Trajectory Optimization for Offline Model-Based Reinforcement Learning
Guanquan Wang, Takuya Hiraoka, Yoshimasa Tsuruoka
This paper introduces Consistency Trajectory Planning (CTP), a novel offline model-based reinforcement learning method that leverages the recently proposed Consistency Trajectory M…
Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
Takuya Hiraoka, Guanquan Wang, Takashi Onishi +1
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about how these experiences influence…
Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning
Shumpei Kubosawa, Takashi Onishi, Makoto Sakahara +1
The number of railway service disruptions has been increasing owing to intensification of natural disasters. In addition, abrupt changes in social situations such as the COVID-19 p…