Add to ORKGAnimals that fail to predict or control events associated with rewards and punishments are not long for this world. Reinforcement learning thus offers a body of theory that organizes and motivates a huge wealth of work in psychology and neuroscience. Equally, these latter disciplines provide inspiration for new methods, ideas and problems in the wider field of reinforcement learning. I will discuss this consilience, illustrating the fecundity of the approaches and some of the challenges and opportunities ahead
Reinforcement Learning describes a general method for trial-and-error learning, and has emerged as a...
In this article we will compare traditional reinforcement learning techniques with a novel correlati...
I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several a...
Reinforcement learning has become a wide and deep conduit that links ideas and results in computer s...
The field of Reinforcement Learning (RL) was inspired in large part by research in animal behavior a...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
Here we review recent developments in the application of reinforcement-learning theory as a means of...
Learning from positive and negative consequences of self-generated behavior is fundamental for secur...
A central problem in cognitive neuroscience is how animals can manage to rapidly master complex sens...
AbstractThe impulsive preference of an animal for an immediate reward implies that it might subjecti...
Neural computational accounts of reward-learning have been dominated by the hypothesis that dopamine...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
■ Our ability to make decisions is predicated upon our knowl-edge of the outcomes of the actions ava...
This paper surveys the field of reinforcement learning from a computer-science perspective. It is wr...
Reinforcement Learning describes a general method for trial-and-error learning, and has emerged as a...
In this article we will compare traditional reinforcement learning techniques with a novel correlati...
I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several a...
Reinforcement learning has become a wide and deep conduit that links ideas and results in computer s...
The field of Reinforcement Learning (RL) was inspired in large part by research in animal behavior a...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
Here we review recent developments in the application of reinforcement-learning theory as a means of...
Learning from positive and negative consequences of self-generated behavior is fundamental for secur...
A central problem in cognitive neuroscience is how animals can manage to rapidly master complex sens...
AbstractThe impulsive preference of an animal for an immediate reward implies that it might subjecti...
Neural computational accounts of reward-learning have been dominated by the hypothesis that dopamine...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
■ Our ability to make decisions is predicated upon our knowl-edge of the outcomes of the actions ava...
This paper surveys the field of reinforcement learning from a computer-science perspective. It is wr...
Reinforcement Learning describes a general method for trial-and-error learning, and has emerged as a...
In this article we will compare traditional reinforcement learning techniques with a novel correlati...
I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several a...