Denis Yarats

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 …

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

2022 · arXiv (Cornell University)

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors. CIC utilizes contrastive learning between state-transitions and skills to learn behavior embeddings …

Watch and Match: Supercharging Imitation with Regularized Optimal Transport

2022 · arXiv (Cornell University)

Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a …

Convolutional Sequence to Sequence Learning

2017 · arXiv (Cornell University)

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent …