Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of
investigations of decision-making, behavioral flexibility, and social behavior. We review …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

Deep reinforcement learning (RL) methods have driven impressive advances in artificial
intelligence in recent years, exceeding human performance in domains ranging from Atari to …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Over the past 20 years, neuroscience research on reward-based learning has converged on
a canonical model, under which the neurotransmitter dopamine 'stamps in'associations …

Despite recent breakthroughs in the applications of deep neural networks, one setting that
presents a persistent challenge is that of" one-shot learning." Traditional gradient-based …

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 …