In recent years, brain science and neuroscience have greatly propelled the innovation of
computer science. In particular, knowledge from the neurobiology and neuropsychology of …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

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 (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 …

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 …

In the last 15 years, there has been a flourishing of research into the neural basis of
reinforcement learning, drawing together insights and findings from psychology, computer …

We provide a framework to accelerate reinforcement learning (RL) algorithms by heuristics
that are constructed by domain knowledge or offline data. Tabula rasa RL algorithms require …

Reinforcement learning has a rich history in neuroscience, from early work on dopamine as
a reward prediction error signal for temporal difference learning (Schultz et al., 1997) to …

We present OpenRL, an advanced reinforcement learning (RL) framework designed to
accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent …

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