Haotian Fu (傅浩天)

Haotian Fu (傅浩天)

Computer Science PhD student at Brown University

Hi, I’m Haotian! I’m a Computer Science PhD student at Brown University, advised by Prof. George Konidaris. I also work closely with Prof. Michael Littman. Currently I’m a research intern at the Boston Dynamics AI Institute. I received my bachelor degree from Tianjin University, advised by Prof. Jianye Hao. I was a visiting student at UC Berkeley in 2018. I have also spent time as a research intern at Microsoft Research, working with Nicolas Le Roux, Xindi Yuan, Marc-Alexandre Côté.

Interests
  • Embodied agents
  • Reinforcement Learning
  • Meta/continual Learning
  • Robotics

Publications

(2024). EPO:Hierarchical LLM Agents with Environment Preference Optimization. In The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024).

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(2024). Language-guided Skill Learning with Temporal Variational Inference. In Forty-first International Conference on Machine Learning (ICML), 2024.

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(2024). Model-based Reinforcement Learning for Parameterized Action Spaces. In Forty-first International Conference on Machine Learning (ICML), 2024.

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(2023). Meta-learning Parameterized Skills. In Fortieth International Conference on Machine Learning (ICML), 2023.

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(2023). Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. In Proceedings of the Eleventh International Conference on Learning Representations (ICLR), 2023.

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(2023). A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. In Neural Networks, January 2023.

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(2022). Model-based Lifelong Reinforcement Learning with Bayesian Exploration. In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

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(2021). Towards Effective Context for Meta Reinforcement Learning: an Approach based on Contrastive Learning. In AAAI 2021.

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(2020). MGHRL: Meta Goal generation for Hierarchical Reinforcement Learning. In Beyond Tabula Rasa in Reinforcement Learning workshop at ICLR , 2020. In DAI 2020..

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(2019). Deep Multi Agent Reinforcement Learning with Discrete Continuous Hybrid Action Spaces. In IJCAI 2019.

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