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Found 13 papers, 0 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.
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.
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure
1, The type of activation function is single and relatively fixed, which leads to poor "unit representation ability" of the network, and it is often used to solve simple problems with very complex networks; 2, the network structure is not adaptive, it is easy to cause network structure redundant or insufficient.
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.
MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction.
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL algorithms.
Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience.
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data.
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.
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
Many exploration strategies are built upon the optimism in the face of the uncertainty (OFU) principle for reinforcement learning.
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks.
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning
Recently, the principle of optimism in the face of (aleatoric and epistemic) uncertainty has been utilized to design efficient exploration strategies for Reinforcement Learning (RL).
