Nicolas Bougie

According to our database, Nicolas Bougie authored at least 14 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Interpretable Imitation Learning with Symbolic Rewards.

Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka

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

2023

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

Nicolas Bougie, Daria Vazhenina, Narimasa Watanabe

Proceedings of the Asian Conference on Machine Learning, 2023

2022

Hierarchical learning from human preferences and curiosity.

Nicolas Bougie, Ryutaro Ichise

Appl. Intell., 2022

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

Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka

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

2021

Efficient Reinforcement Learning through Improved Cognitive Capabilities.

Nicolas Bougie

PhD thesis, 2021

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

Nicolas Bougie, Ryutaro Ichise

Appl. Intell., 2021

Goal-driven active learning.

Nicolas Bougie, Ryutaro Ichise

Auton. Agents Multi Agent Syst., 2021

2020

Skill-based curiosity for intrinsically motivated reinforcement learning.

Nicolas Bougie, Ryutaro Ichise

Mach. Learn., 2020

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

Nicolas Bougie, Ryutaro Ichise

IEICE Trans. Inf. Syst., 2020

Intrinsically Motivated Lifelong Exploration in Reinforcement Learning.

Nicolas Bougie, Ryutaro Ichise

Proceedings of the Advances in Artificial Intelligence, 2020

Towards High-Level Intrinsic Exploration in Reinforcement Learning.

Nicolas Bougie, Ryutaro Ichise

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

Exploration via Progress-Driven Intrinsic Rewards.

Nicolas Bougie, Ryutaro Ichise

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

2018

Deep reinforcement learning boosted by external knowledge.

Nicolas Bougie, Ryutaro Ichise

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

Abstracting Reinforcement Learning Agents with Prior Knowledge.

Nicolas Bougie, Ryutaro Ichise

Proceedings of the PRIMA 2018: Principles and Practice of Multi-Agent Systems - 21st International Conference, Tokyo, Japan, October 29, 2018