19 citations · 29 across the 7 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2021★ 2 cited
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Meta-reinforcement learning (RL) addresses the problem of sample inefficiency in deep RL by using experience obtained in past tasks for a new task to be solved. However, most meta-…
cs.AI2018★ 1 cited
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa +1
Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of…