Aug 9, 2023 · In this paper, we propose a novel framework called Meta-Task (MeTask) for offline RL that leverages meta-learning techniques to learn a task representation.
May 6, 2024 · In this paper, we propose a novel framework called Meta-Task (MeTask) for offline RL that leverages meta-learning techniques to learn a task representation.
These results suggest that leveraging task diversity and meta-learning techniques can significantly improve the efficiency of offline RL methods. Keywords: Meta ...
The main goal of this study is to improve the learning rate of the agent by transferring the relevant parts of the knowledge acquired as a result of previous ...
Task Inference for Offline Meta Reinforcement Learning via Latent Shared Knowledge. https://doi.org/10.1007/978-3-031-40292-0_29 ·. Journal: Knowledge Science ...
Mar 12, 2024 · Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset.
First, in the task inference module, we realize repre- sentative and robust task inference using the Gaussian mix- ture latent space for task representation and ...
May 6, 2024 · COSTA addresses two key challenges in offline safe meta RL: First, it develops a cost-aware task inference module using contrastive learning to ...
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What is meta learning in reinforcement learning?
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Our approach utilizes a context-based meta-RL architecture, comprising a task inference module and a conditional policy module. The key insight of our method is ...
This paper introduces the offline meta- reinforcement learning (offline meta-RL) problem setting and proposes an algorithm that.