The field of Reinforcement Learning (RL) was inspired in large part by research in animal behavior and psychology. Early research showed that animals can, through trial and error, learn to execute behavior that would eventually lead to some (presumably satisfactory) outcome, and decades of subsequent research was (and is still) aimed at dis-covering the mechanisms of this learning process. This chapter describes behavioral and theoretical research in animal learning that is directly related to fundamental concepts used in RL. It then describes neuroscientific research that suggests that animals and many RL algorithms use very similar learning mechanisms. Along the way, I highlight ways that research in computer science contributes to and ca...
Reinforcement sensitivity is a concept proposed by Gray (1973) to describe the biological antecedent...
As Baum argues, reinforcement learning is essential to intelligence (Baum 2004). It enabled humans w...
Animal learning is based on a process of trial and error. This is a fundamental observation in behav...
Animals that fail to predict or control events associated with rewards and punishments are not long ...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
Reinforcement learning has become a wide and deep conduit that links ideas and results in computer s...
This paper surveys the field of reinforcement learning from a computer-science perspective. It is wr...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
Machine learning plays a pivotal role in artificial intelligence, allowing machines to mimic human l...
Brains rule the world, and brain-like computation is increasingly used in computers and electronic d...
This chapter gives a compact, self{contained tutorial survey of reinforcement learn-ing, a tool that...
A central problem in cognitive neuroscience is how animals can manage to rapidly master complex sens...
To what extent are the processes of human learning analogous to the more ele-mentary learning proces...
Reinforcement sensitivity is a concept proposed by Gray (1973) to describe the biological antecedent...
Abstract: It was once taken for granted that learning in animals and man could be explained with a s...
Reinforcement sensitivity is a concept proposed by Gray (1973) to describe the biological antecedent...
As Baum argues, reinforcement learning is essential to intelligence (Baum 2004). It enabled humans w...
Animal learning is based on a process of trial and error. This is a fundamental observation in behav...
Animals that fail to predict or control events associated with rewards and punishments are not long ...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
Reinforcement learning has become a wide and deep conduit that links ideas and results in computer s...
This paper surveys the field of reinforcement learning from a computer-science perspective. It is wr...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
Machine learning plays a pivotal role in artificial intelligence, allowing machines to mimic human l...
Brains rule the world, and brain-like computation is increasingly used in computers and electronic d...
This chapter gives a compact, self{contained tutorial survey of reinforcement learn-ing, a tool that...
A central problem in cognitive neuroscience is how animals can manage to rapidly master complex sens...
To what extent are the processes of human learning analogous to the more ele-mentary learning proces...
Reinforcement sensitivity is a concept proposed by Gray (1973) to describe the biological antecedent...
Abstract: It was once taken for granted that learning in animals and man could be explained with a s...
Reinforcement sensitivity is a concept proposed by Gray (1973) to describe the biological antecedent...
As Baum argues, reinforcement learning is essential to intelligence (Baum 2004). It enabled humans w...
Animal learning is based on a process of trial and error. This is a fundamental observation in behav...