Search Results for author:
Found 11 papers, 3 papers with code
SheetAgent: A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
To bridge the gap with the real-world requirements, we introduce $\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges.
Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models
Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes.
MENTOR: Guiding Hierarchical Reinforcement Learning with Human Feedback and Dynamic Distance Constraint
To address the issue, We propose a general hierarchical reinforcement learning framework incorporating human feedback and dynamic distance constraints (MENTOR).
Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
It is crucial to consider diverse human feedback types and various learning methods in different environments.
DiffuserLite: Towards Real-time Diffusion Planning
Diffusion planning has been recognized as an effective decision-making paradigm in various domains.
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).
Dissimilar Nodes Improve Graph Active Learning
The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks.
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
ED2: Environment Dynamics Decomposition World Models for Continuous Control
Model-based reinforcement learning (MBRL) achieves significant sample efficiency in practice in comparison to model-free RL, but its performance is often limited by the existence of model prediction error.
SAPIEN: A SimulAted Part-based Interactive ENvironment
To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.
