2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
Unsupervised Discovery of Continuous Skills on a Sphere
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Recently, methods for learning diverse skills to generate various behaviors without external rewards have been actively studied as a form of unsupervised reinforcement learning. Ho…
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout
Takuya Hiraoka, Takashi Onishi, Yoshimasa Tsuruoka
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about the influence is valuable for v…