There is and has been a fruitful flow of concepts and ideas between studies of learning in
biological and artificial systems. Much early work that led to the development of …

Reinforcement learning (RL) is a set of mathematical methods and algorithms that can be
applied to a wide array of problems and plays a central role in machine learning. The aim of …

Reinforcement learning is a computational framework for an active agent to learn behaviors
on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

Reinforcement learning algorithms have provided some of the most influential computational
theories for behavioral learning that depends on reward and penalty. After briefly reviewing …

Reinforcement learning methods have recently been very successful at performing complex
sequential tasks like playing Atari games, Go and Poker. These algorithms have …

Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this
dissertation is to extend the state of the art of reinforcement learning and enable its …

Reinforcement learning refers to ways of improving performance through trial-and-error
experience. Despite recent progress in developing artificial learning systems, including new …

Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …