Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …

Abstract In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly
in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms …

A critical problem with the practical utility of controllers trained with deep Reinforcement
Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies …

Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
For example, most RL algorithms collect new data throughout training, using a non …

The success of deep learning in the computer vision and natural language processing
communities can be attributed to training of very deep neural networks with millions or …

Sampling is ubiquitous in machine learning methodologies. Due to the growth of large
datasets and model complexity, we want to learn and adapt the sampling process while …

Deep reinforcement learning (RL) can enable robots to autonomously acquire complex
behaviors, such as legged locomotion. However, RL in the real world is complicated by …