In reinforcement learning (RL), a decision maker searching for the most rewarding option is
often faced with the question: What is the value of an option that has never been tried …

The ability to adapt behavior to maximize reward as a result of interactions with the
environment is crucial for the survival of any higher organism. In the framework of …

Reinforcement learning (RL) algorithms provide powerful explanations for simple learning
and decision-making behaviors and the functions of their underlying neural substrates …

Reinforcement learning has achieved broad and successful application in cognitive science
in part because of its general formulation of the adaptive control problem as the …

Highlights•Standard RL explains some aspects of operant learning and its underlying neural
activity.•Nevertheless, some operant learning behaviors seem inconsistent with standard …

Changes of synaptic connections between neurons are thought to be the physiological basis
of learning. These changes can be gated by neuromodulators that encode the presence of …

Recently there has been a good deal of interest in using techniques developed for learning
from reinforcement to guide learning in robots. Motivated by the desire to find better robot …

Abstract The combination of Reinforcement Learning (RL) with deep learning has led to a
series of impressive feats, with many believing (deep) RL provides a path towards generally …

This paper surveys the field of reinforcement learning from a computer—science per—
spective. It is written to be accessible to researchers familiar with machine learning. Both the …

Reinforcement learning is the process by which an agent learns to predict long-term future
reward. We now understand a great deal about the brain's reinforcement learning …