2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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-…
Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System
Hisao Katsumi, Takuya Hiraoka, Koichiro Yoshino +4
Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient suppo…
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…
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks
Seydou Ba, Takuya Hiraoka, Takashi Onishi +2
Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions. For an effective action selectio…
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings
Takuya Hiraoka, Masaaki Tsuchida, Yotaro Watanabe
This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and di…