Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation

Information Science, Computer Science, Artificial Intelligence,
empathetic response generation   reinforcement learning   empathy communication mechanism   reward function   dialogue systems  

Research on Empathetic Response Generation in AI Dialogue Systems Academic Background With the rapid development of artificial intelligence technology, open-domain dialogue systems have gradually become a research hotspot. These systems aim to engage in natural and fluent conversations with users, providing reasonable responses. However, despite si...

Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment with Relational Graph Learning

Engineering, Mechanical Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
multiagent system   reinforcement learning   relational graph learning   hybrid environment   cooperative navigation   decentralized execution   collision avoidance  

Multi-agent Cooperative Navigation in Hybrid Environments: A New Reinforcement Learning Approach Based on Relational Graph Learning Mobile robotics is witnessing a surge in applications, fueled by advancements in artificial intelligence, with navigation capabilities being one of the core focus areas of research. Traditional navigation methods often...

Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Engineering, Mechanical Engineering, Ocean Engineering, Transportation Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
adaptive control   fixed-time control   reinforcement learning   nonlinear systems   neural networks   intelligent ships   optimization methods  

Nonlinear Fixed-Time Reinforcement Learning Optimized Control for Intelligent Ship Autopilots In recent years, intelligent autopilot technology has gradually become a research hotspot in the field of automation control. For complex nonlinear systems, the design of optimized control strategies, especially the achievement of system stability and perf...

Q-Cogni: An Integrated Causal Reinforcement Learning Framework

Information Science, Computer Science, Artificial Intelligence,
causal inference   reinforcement learning   vehicle routing problem   autonomous causal structure discovery   model-free learning   model-based learning   interpretability  

Research Insight Report: Q-Cogni—An Integrated Causal Reinforcement Learning Framework In recent years, the rapid advancement of artificial intelligence (AI) has propelled researchers to explore the development of more efficient and interpretable reinforcement learning (RL) systems. Due to its ability to mimic human decision-making, reinforcement l...

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-throughput Screening and Reinforcement Learning

Chemistry, Organic Chemistry, Medicinal Chemistry, Medicine, Infectious Diseases,
A. baumannii   coumarins   lung infection   high-throughput screening   reinforcement learning   structure optimization  

Review of Research on Treatment of Lung Infections with Novel Coumarin Derivatives Background With the increasing antibiotic resistance, especially the problem of Acinetobacter baumannii’s resistance to antibiotics, researchers worldwide have begun to search for new antimicrobial agents. This Gram-negative bacterium, with its high survivability and...

Comparing Experience- and Description-based Economic Preferences Across 11 Countries

Humanities and Others,
experience-based   description-based   economic preferences   cross-cultural study   reinforcement learning  

Comparison of Experiences and Descriptions of Basic Economic Preferences in 11 Countries Background and Motivation Recent studies have shown that humans exhibit a high degree of context-dependency in encoding the value of rewards, which in some cases leads to suboptimal decisions. However, it remains unclear whether this computational limitation is...

Exploration-based Model Learning with Self-Attention for Risk-Sensitive Robot Control

Engineering, Mechanical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
robot control   self-attention   model learning   risk-sensitive   reinforcement learning   dynamics model   kinematics model  

Discussion on Risk-Sensitive Robot Control Based on Self-Attention Mechanism Research Background The kinematics and dynamics in robot control are key factors to ensure the precise completion of tasks. Most robot control schemes rely on various models to achieve task optimization, scheduling, and priority control. However, the dynamic characteristic...

Modeling Bellman-Error with Logistic Distribution with Applications in Reinforcement Learning

Mathematics, Information Science, Artificial Intelligence,
reinforcement learning   logistic distribution   reward scaling   bellman error  

Background and Research Objectives Reinforcement Learning (RL) has recently become a dynamic and transformative field within artificial intelligence, aiming to maximize cumulative rewards through the interaction between agents and the environment. However, the application of RL faces challenges in optimizing the Bellman Error. This error is particu...

Real-World Humanoid Locomotion with Reinforcement Learning

Real-World Humanoid Locomotion with Reinforcement Learning

Information Science, Automation, Artificial Intelligence,
humanoid robot   reinforcement learning   robot locomotion   environment adaptation   causal transformer   external disturbance   domain randomization  

Real-World Humanoid Robot Walking Based on Reinforcement Learning Background Introduction Humanoid robots have enormous potential for autonomous operation in diverse environments, not only alleviating labor shortages in factories but also assisting elderly people at home and exploring new planets. Although classical controllers show excellent effec...

Robust Multiobjective Reinforcement Learning Considering Environmental Uncertainties

Information Science, Artificial Intelligence,
reinforcement learning   multiobjective optimization   adversarial machine learning   decision and control   environmental disturbances  

Background In recent years, Reinforcement Learning (RL) has demonstrated its effectiveness in solving various complex tasks. However, many real-world decision-making and control problems involve multiple conflicting objectives. The relative importance (preference) of these objectives often needs to be balanced against each other in different scenar...