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2020 – today
- 2025
[i5]Nicolas Bougie, Narimasa Watanabe:
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation. CoRR abs/2504.12722 (2025)- [i4]Xiaotong Ye, Nicolas Bougie, Toshihiko Yamasaki, Narimasa Watanabe:
MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation. CoRR abs/2504.16946 (2025) - [i3]Nicolas Bougie, Narimasa Watanabe:
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation. CoRR abs/2506.21805 (2025) - 2024
- [j6]Nicolas Bougie
, Takashi Onishi, Yoshimasa Tsuruoka:
Interpretable Imitation Learning with Symbolic Rewards. ACM Trans. Intell. Syst. Technol. 15(1): 4:1-4:34 (2024) - [c9]Nicolas Bougie, Narimasa Watanabe:
Exploring Beyond Curiosity Rewards: Language-Driven Exploration in RL. ACML 2024: 127-142 - [i2]Nicolas Bougie, Narimasa Watanabe:
Generative Adversarial Reviews: When LLMs Become the Critic. CoRR abs/2412.10415 (2024) - 2023
- [c8]Nicolas Bougie, Daria Vazhenina, Narimasa Watanabe:
Lost and Found: How Self-Supervised Learning Helps GPS Coordinates Find Their Way. ACML 2023: 185-200 - 2022
- [j5]Nicolas Bougie, Ryutaro Ichise:
Hierarchical learning from human preferences and curiosity. Appl. Intell. 52(7): 7459-7479 (2022) - [c7]Nicolas Bougie, Ryutaro Ichise:
Goal-Driven Active Learning. AAMAS 2022: 1923-1925 - [c6]Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka:
Local Control is All You Need: Decentralizing and Coordinating Reinforcement Learning for Large-Scale Process Control. SICE 2022: 468-474 - 2021
- [b1]Nicolas Bougie:
Efficient Reinforcement Learning through Improved Cognitive Capabilities. Graduate University for Advanced Studies, Japan, 2021 - [j4]Nicolas Bougie, Ryutaro Ichise:
Goal-driven active learning. Auton. Agents Multi Agent Syst. 35(2): 44 (2021) - [j3]Nicolas Bougie, Ryutaro Ichise:
Fast and slow curiosity for high-level exploration in reinforcement learning. Appl. Intell. 51(2): 1086-1107 (2021) - 2020
- [j2]Nicolas Bougie, Ryutaro Ichise:
Towards Interpretable Reinforcement Learning with State Abstraction Driven by External Knowledge. IEICE Trans. Inf. Syst. 103-D(10): 2143-2153 (2020) - [j1]Nicolas Bougie, Ryutaro Ichise:
Skill-based curiosity for intrinsically motivated reinforcement learning. Mach. Learn. 109(3): 493-512 (2020) - [c5]Nicolas Bougie, Ryutaro Ichise:
Exploration via Progress-Driven Intrinsic Rewards. ICANN (2) 2020: 269-281 - [c4]Nicolas Bougie, Ryutaro Ichise:
Towards High-Level Intrinsic Exploration in Reinforcement Learning. IJCAI 2020: 5186-5187 - [c3]Nicolas Bougie, Ryutaro Ichise:
Intrinsically Motivated Lifelong Exploration in Reinforcement Learning. JSAI 2020: 109-120
2010 – 2019
- 2018
- [c2]Nicolas Bougie, Ryutaro Ichise:
Abstracting Reinforcement Learning Agents with Prior Knowledge. PRIMA 2018: 431-439 - [c1]Nicolas Bougie, Ryutaro Ichise:
Deep reinforcement learning boosted by external knowledge. SAC 2018: 331-338 - 2017
- [i1]Nicolas Bougie, Ryutaro Ichise:
Deep Reinforcement Learning Boosted by External Knowledge. CoRR abs/1712.04101 (2017)
Coauthor Index
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