19 citations · 51 across the 17 of their papers we have counts for
17 papers
Learning Stateful Predictive Knowledge From Experience
Yan Song, Xidong Feng, Bo Liu +7
As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predicti…
From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang, Haotian Fu, Mingxi Jia +3
We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior ma…
SkillWrapper: Generative Predicate Invention for Task-level Robot Planning
Ziyi Yang, Benned Hedegaard, Ahmed Jaafar +8
Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic repres…
Learning Parameterized Skills from Demonstrations
Vedant Gupta, Haotian Fu, Calvin Luo +2
We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy…
Data-Efficient Multitask DAgger
Haotian Fu, Ran Gong, Xiaohan Zhang +3
Generalist robot policies that can perform many tasks typically require extensive expert data or simulations for training. In this work, we propose a novel Data-Efficient multitask…
Knowledge Retention for Continual Model-Based Reinforcement Learning
Yixiang Sun, Haotian Fu, Michael Littman +1
We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that d…
Natural Language Reinforcement Learning
Xidong Feng, Bo Liu, Yan Song +7
Artificial intelligence progresses towards the "Era of Experience," where agents are expected to learn from continuous, grounded interaction. We argue that traditional Reinforcemen…
EPO: Hierarchical LLM Agents with Environment Preference Optimization
Qi Zhao, Haotian Fu, Chen Sun +1
Long-horizon decision-making tasks present significant challenges for LLM-based agents due to the need for extensive planning over multiple steps. In this paper, we propose a hiera…
Model-based Reinforcement Learning for Parameterized Action Spaces
Renhao Zhang, Haotian Fu, Yilin Miao +1
We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Dec…
Natural Language Reinforcement Learning
Xidong Feng, Ziyu Wan, Mengyue Yang +5
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, la…
Language-guided Skill Learning with Temporal Variational Inference
Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin +4
We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajecto…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Haotian Fu, Shangqun Yu, Michael Littman +1
We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. Th…
Meta-Learning Parameterized Skills
Haotian Fu, Shangqun Yu, Saket Tiwari +2
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient l…
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao +4
Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Me…
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Haotian Fu, Hongyao Tang, Jianye Hao +2
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work w…
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Haotian Fu, Hongyao Tang, Jianye Hao +3
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However,…