Deep reinforcement learning has contributed to dramatic advances in many tasks, such as
playing games, controlling robots, and navigating complex environments. However, it …

Abstract Automated Reinforcement Learning (AutoRL) is a relatively new area of research
that is gaining increasing attention. The objective of AutoRL consists in easing the …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

In many Reinforcement Learning (RL) papers, learning curves are useful indicators to
measure the effectiveness of RL algorithms. However, the complete raw data of the learning …

Though deep reinforcement learning has led to breakthroughs in many difficult domains,
these successes have required an ever-increasing number of samples. As state-of-the-art …

Reinforcement Learning (RL) has been effectively used to solve complex problems given
careful design of the problem and algorithm parameters. However standard RL approaches …

In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep
RL algorithm. BAC mathematically formulates the behavior of the policy through …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

The offline reinforcement learning (RL) problem, also referred to as batch RL, refers to the
setting where a policy must be learned from a dataset of previously collected data, without …

Get hands-on experience in creating state-of-the-art reinforcement learning agents using
TensorFlow and RLlib to solve complex real-world business and industry problems with the …