Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

In recent years, significant progress has been made in the field of robotic reinforcement
learning (RL), enabling methods that handle complex image observations, train in the real …

Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of
complex robotic manipulation skills, but realizing this potential in real-world settings has …

Humans excel at efficiently navigating through crowds without collision by focusing on
specific visual regions relevant to navigation. However, most robotic visual navigation …

As autonomous driving technology advances, researchers are focusing on utilizing expert
priors to improve the agents for learning-based decision-making in autonomous vehicles …

In practice, reinforcement learning (RL) agents are often trained with a possibly imperfect
proxy reward function, which may lead to a human-agent alignment issue (ie, the learned …

Imitation learning techniques have been shown to be highly effective in real-world control
scenarios, such as robotics. However, these approaches not only suffer from compounding …

Vision-Language-Action (VLA) models have shown substantial potential in real-world
robotic manipulation. However, fine-tuning these models through supervised learning …

In the era of deep learning, the natural language processing (NLP) community has become
increasingly reliant on large language models (LLM), which are essentially probabilistic …

This research explores the application of policy gradient methods in multi-agent
reinforcement learning, augmented with offline reinforcement Learning techniques. The goal …