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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
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications.
Reinforcement Learning from Bagged Reward
In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative rewards to obtain the optimal policy.
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.
