Constrained Meta Agnostic Reinforcement Learning
<|reference_start|>Constrained Meta Agnostic Reinforcement Learning: 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.<|reference_end|>
@article{daaboul2024constrained,
    title={Constrained Meta Agnostic Reinforcement Learning},
    author={Karam Daaboul, Florian Kuhm, Tim Joseph, J. Marius Zoellner},
    journal={arXiv preprint arXiv:2406.14047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14047},
    primaryClass={cs.LG}
}