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 …

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 …

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 …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

In reinforcement learning, it is common to let an agent interact for a fixed amount of time with
its environment before resetting it and repeating the process in a series of episodes. The …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Abstract Model-based Reinforcement Learning (MBRL) is a promising framework for
learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to …

Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

While current benchmark reinforcement learning (RL) tasks have been useful to drive
progress in the field, they are in many ways poor substitutes for learning with real-world …

State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly
interact with their environment to collect millions of observations. This makes it hard to …