Feb 20, 2024 · We propose a new perspective to the offline reinforcement learning (RL) challenge. More concretely, we transform it into a supervised learning task.
We propose a new perspective to the offline reinforcement learning (RL) challenge. More concretely, we transform it into a supervised learning task.
May 20, 2024 · We introduce a novel approach that aligns multimodal models with pre-trained sequence models for sequential RL, thereby enhancing. RL training ...
Feb 20, 2024 · This study argues that the latent state representations derived from images during offline RL training, coupled with the discrete symbolic ...
This work transforms offline reinforcement learning into a supervised learning task by integrating multimodal and pre-trained language models, ...
Abstract: Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions ...
Feb 20, 2024 · multimodal and pre-trained language models. Our approach incorporates state information derived from images and action-related data ob- tained ...
MORE: Multimodal-based Offline Reinforcement lEarning. License. MIT license · 3 stars 0 forks Branches Tags Activity.
People also ask
What is the difference between online and offline reinforcement learning?
What are the three types of machine learning supervised learning unsupervised learning reinforcement learning all of the above?
In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement ...
Multi-Task Learning. 21. Paper · Code · MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces · 1 code implementation • 20 Feb ...