Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded
datasets without online environment interaction. It is unfortunate that extrapolation errors can …

Abstract The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …

Conventional state representations in reinforcement learning often omit critical task-related
details, presenting a significant challenge for value networks in establishing accurate …

Variable speed limit (VSL) control benefits freeway operations through dynamic speed limit
adjustment strategies for specific operation scenarios, such as traffic jams, secondary crash …

The development of intelligent driving systems has drawn significant attention to enhancing
the safety of autonomous vehicles and their intended functionality. Despite this, current …

Offline Reinforcement Learning (RL) suffers from the extrapolation error and value
overestimation. From a generalization perspective, this issue can be attributed to the over …

Recent progress in generative models has stimulated significant innovations in many fields,
such as image generation and chatbots. Despite their success, these models often produce …

Meta learning is a promising paradigm in the era of large models and task distributional
robustness has become an indispensable consideration in real-world scenarios. Recent …

In multi-agent reinforcement learning (MARL), the low quality of value function and the
estimation bias and variance in value function decomposition (VFD) are critical challenges …