Dataset Distillation for Offline Reinforcement Learning
<|reference_start|>Dataset Distillation for Offline Reinforcement Learning: Offline reinforcement learning often requires a quality dataset that we can train a policy on. However, in many situations, it is not possible to get such a dataset, nor is it easy to train a policy to perform well in the actual environment given the offline data. We propose using data distillation to train and distill a better dataset which can then be used for training a better policy model. We show that our method is able to synthesize a dataset where a model trained on it achieves similar performance to a model trained on the full dataset or a model trained using percentile behavioral cloning. Our project site is available at $\href{https://datasetdistillation4rl.github.io}{\text{here}}$. We also provide our implementation at $\href{https://github.com/ggflow123/DDRL}{\text{this GitHub repository}}$.<|reference_end|>
@article{light2024dataset,
    title={Dataset Distillation for Offline Reinforcement Learning},
    author={Jonathan Light, Yuanzhe Liu, Ziniu Hu},
    journal={arXiv preprint arXiv:2407.20299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20299},
    primaryClass={cs.LG cs.AI}
}