Ilya Kostrikov

Automatic Data Augmentation for Generalization in Reinforcement Learning

2021 · arXiv (Cornell University)

Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments. To alleviate this problem, recent work has proposed the use of data augmentation. However, different tasks tend to benefit from different types …

Efficient Deep Reinforcement Learning Requires Regulating Overfitting

2023 · arXiv (Cornell University)

Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial …

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning

2024 · arXiv (Cornell University)

Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large pre-collected datasets. This in turn can facilitate real-world applications, as …