Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
<|reference_start|>Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings: Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner? In this work, we present a functional reward encoding (FRE) as a general, scalable solution to this zero-shot RL problem. Our main idea is to learn functional representations of any arbitrary tasks by encoding their state-reward samples using a transformer-based variational auto-encoder. This functional encoding not only enables the pre-training of an agent from a wide diversity of general unsupervised reward functions, but also provides a way to solve any new downstream tasks in a zero-shot manner, given a small number of reward-annotated samples. We empirically show that FRE agents trained on diverse random unsupervised reward functions can generalize to solve novel tasks in a range of simulated robotic benchmarks, often outperforming previous zero-shot RL and offline RL methods. Code for this project is provided at: https://github.com/kvfrans/fre<|reference_end|>
@article{frans2024unsupervised,
    title={Unsupervised Zero-Shot Reinforcement Learning via Functional Reward
  Encodings},
    author={Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine},
    journal={arXiv preprint arXiv:2402.17135},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17135},
    primaryClass={cs.LG cs.AI}
}