Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

This article reviews meta-learning also known as learning-to-learn which seeks rapid and
accurate model adaptation to unseen tasks with applications in highly automated AI, few …

This work presents FG-Net, a general deep learning framework for large-scale point cloud
understanding without voxelizations, which achieves accurate and real-time performance …

The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn
new tasks by leveraging prior experience on related tasks. Learning a new task often …

Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning
algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind …

While meta reinforcement learning (Meta-RL) methods have achieved remarkable success,
obtaining correct and low variance estimates for policy gradients remains a significant …

The objective of a reinforcement learning agent is to behave so as to maximise the sum of a
suitable scalar function of state: the reward. These rewards are typically given and …

This work presents FG-Net, a general deep learning framework for large-scale point clouds
understanding without voxelizations, which achieves accurate and real-time performance …

Meta-reinforcement learning (meta-RL) techniques have demonstrated remarkable success
in generalizing deep reinforcement learning across a range of tasks. Nevertheless, these …

Reinforcement learning (RL)-based multi-hop reasoning has become an interpretable way
for knowledge graph reasoning owing to its persuasive explanations for the predicted …