With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …

In recent years, the integration of large language models (LLMs) has revolutionized the field
of robotics, enabling robots to communicate, understand, and reason with human-like …

Multi-modal foundation models and generative AI have demonstrated promising capabilities
in applications across various domains. Recently, Vision-language-action (VLA) models …

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

Large language models (LLMs) have received considerable attention recently due to their
outstanding comprehension and reasoning capabilities, leading to great progress in many …

Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

Autonomous driving technology has made significant strides, with reinforcement learning
(RL) proving crucial due to its superior decision-making capabilities. However, designing …

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 …

Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates
the achievement of complex policies by progressively increasing the task difficulty during …

Large Language Models (LLMs), with their abilities in knowledge acquisition and reasoning,
can potentially enhance the various aspects of Self-adaptive Systems (SAS). Yet, the …