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Found 6 papers, 1 papers with code
Robust Policy Learning via Offline Skill Diffusion
These skills, learned task-agnostically from offline datasets, can accelerate the policy learning process for new tasks.
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
One-shot imitation is to learn a new task from a single demonstration, yet it is a challenging problem to adopt it for complex tasks with the high domain diversity inherent in a non-stationary environment.
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation
In these cross-domain settings, we present a semantic skill translator framework SemTra which utilizes a set of multi-modal models to extract skills from the snippets, and leverages the reasoning capabilities of a pretrained language model to adapt these extracted skills to the target domain.
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs.
Fixed Priority Global Scheduling from a Deep Learning Perspective
Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains.
A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems
A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores.
