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Chenyu Sun

2 accepted papers

2024

CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning

AAAI 2024technical

Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected dataset. Most existing works are to develop sophisticated learning algorithms, with less emphasis on improving the data collection process. Moreover, it is even challenging to extend the single-task setting an…

Cited by 4SourcePDFScholar
2022

CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning

IJCAI 2022poster

In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently remedied it via encoding invariances from raw pixels. Nevertheless, we empirically find that not all samples are equally important and hence simply injecting m…