| Parental Guidance(PG1) |
| ====================== |
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| Links |
| ----- |
| - **Paper on OpenReview:** `Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation <https://openreview.net/forum?id=mFaPH8JZLC>`_ |
| - **GitHub Repository:** `UW Lab GitHub <https://github.com/uw-lab/UWLab>`_ |
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| Authors |
| ------- |
| **Zhengyu Zhang** †, **Quanquan Peng** ‡, **Rosario Scalise** †, **Byron Boots** † |
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| † Paul G Allen School, University of Washington |
| ‡ Shanghai Jiao Tong University |
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| .. image:: ../../source/_static/publications/pg1/pg1.png |
| :alt: Research Illustration |
| :align: center |
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| Abstract |
| -------- |
| Developing robotic agents that can perform well in diverse environments while showing a variety of behaviors is |
| a key challenge in AI and robotics. Traditional reinforcement learning (RL) methods often create agents that specialize |
| in narrow tasks, limiting their adaptability and diversity. To overcome this, we propose a preliminary, |
| evolution-inspired framework that includes a reproduction module, similar to natural species reproduction, |
| balancing diversity and specialization. By integrating RL, imitation learning (IL), and a coevolutionary agent-terrain |
| curriculum, our system evolves agents continuously through complex tasks. This approach promotes adaptability, |
| inheritance of useful traits, and continual learning. Agents not only refine inherited skills but also surpass |
| their predecessors. Our initial experiments show that this method improves exploration efficiency and supports |
| open-ended learning, offering a scalable solution where sparse reward coupled with diverse terrain environments |
| induces a multi-task setting. |
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| BibTex |
| ---------- |
| .. code:: bibtex |
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| @inproceedings{ |
| zhang2024blending, |
| title={Blending Reinforcement Learning and Imitation Learning for Evolutionary Continual Learning}, |
| author={Zhengyu Zhang and Quanquan Peng and Rosario Scalise and Byron Boots}, |
| booktitle={[CoRL 2024] Morphology-Aware Policy and Design Learning Workshop (MAPoDeL)}, |
| year={2024}, |
| url={https://openreview.net/forum?id=d2VTtWOCMm} |
| } |
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