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 (RL) is a challenging task, whose objective is to learn policies
from static trajectory data without interacting with the environment. Recently, offline RL has …

Prompt-tuning has emerged as a promising method for adapting pre-trained models to
downstream tasks or aligning with human preferences. Prompt learning is widely used in …

Offline safe RL is of great practical relevance for deploying agents in real-world applications.
However, acquiring constraint-satisfying policies from the fixed dataset is non-trivial for …

Designing better deep networks and better reinforcement learning (RL) algorithms are both
important for deep RL. This work studies the former. Specifically, the Perception and …

Recent works have shown the potential of diffusion models in computer vision and natural
language processing. Apart from the classical supervised learning fields, diffusion models …

Designing better deep networks and better reinforcement learning (RL) algorithms are both
important for deep RL. This work focuses on the former. Previous methods build the network …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action …

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy
applicable to diverse tasks without the need for online environmental interaction. Recent …

Recent work has shown that supervised pre-training on learning histories of RL algorithms
results in a model that captures the learning process and is able to improve in-context on …