Search Results for author: Takuya Hiraoka

Found 13 papers, 3 papers with code

Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout

1 code implementation26 Jan 2023 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.

reinforcement-learning reinforcement Learning

Dropout Q-Functions for Doubly Efficient Reinforcement Learning

2 code implementations ICLR 2022 Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka

To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions.

Q-Learning reinforcement-learning +1

Meta-Model-Based Meta-Policy Optimization

no code implementations4 Jun 2020 Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka

Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings.

Continuous Control Meta-Learning +3

Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas

no code implementations IJCNLP 2019 Kosuke Akimoto, Takuya Hiraoka, Kunihiko Sadamasa, Mathias Niepert

Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored.

Relation Extraction

Optimistic Proximal Policy Optimization

no code implementations25 Jun 2019 Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka

Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains.

BIG-bench Machine Learning reinforcement-learning +1

Learning Robust Options by Conditional Value at Risk Optimization

1 code implementation NeurIPS 2019 Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka

While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options.

Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System

no code implementations26 Nov 2018 Hisao Katsumi, Takuya Hiraoka, Koichiro Yoshino, Kazeto Yamamoto, Shota Motoura, Kunihiko Sadamasa, Satoshi Nakamura

It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems often do not have enough of such information in realistic situations.

Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients

no code implementations29 Sep 2018 Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa, Yoshimasa Tsuruoka

In this paper, we propose a framework that can automatically refine symbol grounding functions and a high-level planner to reduce human effort for designing these modules.

Decision Making

Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks

no code implementations7 Sep 2018 Seydou Ba, Takuya Hiraoka, Takashi Onishi, Toru Nakata, Yoshimasa Tsuruoka

The evaluation results show that, with variable simulation times, the proposed approach outperforms the conventional MCTS in the evaluated continuous decision space tasks and improves the performance of MCTS in most of the ALE tasks.

Atari Games

Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings

no code implementations2 Aug 2017 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).

reinforcement-learning reinforcement Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.