Constrained Meta Agnostic Reinforcement Learning

• URL: http://arxiv.org/abs/2406.14047v1
• Date: Thu, 20 Jun 2024 07:11:27 GMT
• Title: Constrained Meta Agnostic Reinforcement Learning
• Authors: Karam Daaboul, Florian Kuhm, Tim Joseph, J. Marius Zoellner,
• Abstract summary: Constraint Model Agnostic Meta Learning (C-MAML) C-MAML enables rapid and efficient task adaptation by incorporating task-specific constraints directly into its meta-algorithm framework during the training phase. We demonstrate the effectiveness of C-MAML in simulated locomotion with wheeled robot tasks of varying complexity, highlighting its practicality and robustness in dynamic environments.
• Score: 2.3749120526936465
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Meta-Reinforcement Learning (Meta-RL) aims to acquire meta-knowledge for quick adaptation to diverse tasks. However, applying these policies in real-world environments presents a significant challenge in balancing rapid adaptability with adherence to environmental constraints. Our novel approach, Constraint Model Agnostic Meta Learning (C-MAML), merges meta learning with constrained optimization to address this challenge. C-MAML enables rapid and efficient task adaptation by incorporating task-specific constraints directly into its meta-algorithm framework during the training phase. This fusion results in safer initial parameters for learning new tasks. We demonstrate the effectiveness of C-MAML in simulated locomotion with wheeled robot tasks of varying complexity, highlighting its practicality and robustness in dynamic environments.

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