Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Develo** smart cities is vital for ensuring sustainable development and improving human
well-being. One critical aspect of building smart cities is designing intelligent methods to …

Language models as intelligent agents push the boundaries of sequential decision-making
agents but struggle with limited knowledge of environmental dynamics and exponentially …

Over recent decades, sequential decision-making tasks are mostly tackled with expert
systems and reinforcement learning. However, these methods are still incapable of being …

Reinforcement learning is able to obtain generalized low-level robot policies on diverse
robotics datasets in embodied learning scenarios, and Transformer has been widely used to …

Heterogeneity in sensors and actuators across environments poses a significant challenge
to building large-scale pre-trained world models on top of this low-dimensional sensor …

This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed
to perform scalable reinforcement learning (RL) with large sequence models (such as …

We propose a novel approach for decision making problems leveraging the generalization
capabilities of large language models (LLMs). Traditional methods such as expert systems …

One of the main challenges of offline Reinforcement Learning is that the difference between
learning policy and behavior policy leads to the possibility that the agent may need to …