As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that
uses standard supervised learning methods as subroutines. Our goal is an algorithm that …

Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …

Dexterous multi-fingered hands are extremely versatile and provide a generic way to
perform a multitude of tasks in human-centric environments. However, effectively controlling …

Exploration in environments with sparse rewards has been a persistent problem in
reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and …