3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.AI2025
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…