Nicolas Bougie

CoRR, June, 2025

MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation.

[BibT_eX]

[DOI]

CoRR, April, 2025

SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation.

[BibT_eX]

[DOI]

CoRR, April, 2025

CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation.

[BibT_eX]

[DOI]

Narimawa Watanabe

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Generative Reviewer Agents: Scalable Simulacra of Peer Review.

[BibT_eX]

[DOI]

Narimawa Watanabe

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation.

[BibT_eX]

[DOI]

Narimawa Watanabe

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), 2025

2024

Interpretable Imitation Learning with Symbolic Rewards.

[BibT_eX]

[DOI]

Takashi Onishi

Yoshimasa Tsuruoka

ACM Trans. Intell. Syst. Technol., February, 2024

Generative Adversarial Reviews: When LLMs Become the Critic.

[BibT_eX]

[DOI]

CoRR, 2024

Exploring Beyond Curiosity Rewards: Language-Driven Exploration in RL.

[BibT_eX]

[DOI]

Proceedings of the Asian Conference on Machine Learning, 2024

2023

Lost and Found: How Self-Supervised Learning Helps GPS Coordinates Find Their Way.

[BibT_eX]

[DOI]

Daria Vazhenina

Proceedings of the Asian Conference on Machine Learning, 2023

2022

Hierarchical learning from human preferences and curiosity.

[BibT_eX]

[DOI]

Appl. Intell., 2022

Local Control is All You Need: Decentralizing and Coordinating Reinforcement Learning for Large-Scale Process Control.

[BibT_eX]

[DOI]

Takashi Onishi

Yoshimasa Tsuruoka

Proceedings of the 61st IEEE Annual Conference of the Society of Instrument and Control Engineers, 2022

Goal-Driven Active Learning.

[BibT_eX]

[DOI]

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

Efficient Reinforcement Learning through Improved Cognitive Capabilities.

[BibT_eX]

[DOI]

PhD thesis, 2021

Fast and slow curiosity for high-level exploration in reinforcement learning.

[BibT_eX]

[DOI]

Appl. Intell., 2021

2020

Skill-based curiosity for intrinsically motivated reinforcement learning.

[BibT_eX]

[DOI]

Mach. Learn., 2020

Towards Interpretable Reinforcement Learning with State Abstraction Driven by External Knowledge.

[BibT_eX]

[DOI]

IEICE Trans. Inf. Syst., 2020

Intrinsically Motivated Lifelong Exploration in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2020

Towards High-Level Intrinsic Exploration in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Exploration via Progress-Driven Intrinsic Rewards.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2018

Deep reinforcement learning boosted by external knowledge.

[BibT_eX]

[DOI]

Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018

Abstracting Reinforcement Learning Agents with Prior Knowledge.

[BibT_eX]

[DOI]