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In this letter we propose to consider both of these approaches together, resulting in a setting where different types of data streams are available and fast online adaptation to new tasks is required.
ieeexplore.ieee.orgIn this work, we seek to get the best of both worlds: we consider the problem of pretraining a world model with offline data collected on a real robot, and then finetuning the model on online data collected by planning with the learned model.
arxiv.orgDuring pre-training, rather than training separate models per task as in previous work [54–56], we train one world model per benchmark and demonstrate that a single multi-task & embodiment world model can effectively leverage non-curated data.
arxiv.orgIn this paper we propose to consider both of these approaches together and argue that this results in a more realistic setting where different types of data are available, and fast online adaptation to new tasks is required.
iclr.ccThe work addresses the challenge of improving sample efficiency in reinforcement learning (RL) by leveraging non-curated offline datasets which are reward-free, of mixed quality, and collected from multiple embodiments during online reinforcement learning.
openreview.netWe propose an algorithm and a data curation method that addresses both of these concerns by demonstrating that effective full-length rollout training is possible without hand-crafted penalties by treating each member of the world model ensemble as a level in the Unsupervised Environment Design (UED) framework.
openreview.netThis paper presents a novel approach that integrates the robustness of UED with the descriptive power of world models, achieving strong test-time performance in a range of environments while training using only offline data.
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