Search Results for author: Takashi Onishi

Found 14 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 (RL)

Soft Sensors and Process Control using AI and Dynamic Simulation

no code implementations8 Aug 2022 Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka

During the operation of a chemical plant, product quality must be consistently maintained, and the production of off-specification products should be minimized.

Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning

no code implementations17 Jan 2022 Shumpei Kubosawa, Takashi Onishi, Makoto Sakahara, Yoshimasa Tsuruoka

The system leverages reinforcement learning and a dynamic simulator that can simulate the railway traffic and passenger flow of a whole line.

reinforcement-learning Reinforcement Learning (RL) +1

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

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.

Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning

no code implementations6 Mar 2019 Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka

Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures.

reinforcement-learning Reinforcement Learning (RL)

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

Hierarchical Reinforcement Learning with Abductive Planning

no code implementations28 Jun 2018 Kazeto Yamamoto, Takashi Onishi, Yoshimasa Tsuruoka

One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utilize prior knowledge on the domain.

Hierarchical Reinforcement Learning reinforcement-learning +1

Translating MFM into FOL: towards plant operation planning

no code implementations19 Jun 2018 Shota Motoura, Kazeto Yamamoto, Shumpei Kubosawa, Takashi Onishi

This paper proposes a method to translate multilevel flow modeling (MFM) into a first-order language (FOL), which enables the utilisation of logical techniques, such as inference engines and abductive reasoners.

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