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Found 6 papers, 0 papers with code
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains
In this paper, we investigate an offline reinforcement learning (RL) problem where datasets are collected from two domains.
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
(3) We achieve the same performance with only 10% of the original data for training as with all of the original data from the real-world dataset, and a better performance on In-the-wild videos, by employing our data augmentation techniques.
Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution
Instead, the learner only obtains rewards at the ends of bags, where a bag is defined as a partial sequence of a complete trajectory.
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding
Learning multi-lingual sentence embeddings is a fundamental task in natural language processing.
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces.
Imitation Learning from Pixel-Level Demonstrations by HashReward
One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space.
