Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Rapid decision threshold modulation by reward rate in a neural network
Neural Networks - 2006 Special issue: Neurobiology of decision making
Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making
Neural Networks - 2006 Special issue: Neurobiology of decision making
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Optimal decision making on the basis of evidence represented in spike trains
Neural Computation
Posterior weighted reinforcement learning with state uncertainty
Neural Computation
Multihypothesis sequential probability ratio tests .I. Asymptotic optimality
IEEE Transactions on Information Theory
A sequential procedure for multihypothesis testing
IEEE Transactions on Information Theory
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This article seeks to integrate two sets of theories describing action selection in the basal ganglia: reinforcement learning theories describing learning which actions to select to maximize reward and decision-making theories proposing that the basal ganglia selects actions on the basis of sensory evidence accumulated in the cortex. In particular, we present a model that integrates the actor-critic model of reinforcement learning and a model assuming that the cortico-basal-ganglia circuit implements a statistically optimal decision-making procedure. The values of corico-striatal weights required for optimal decision making in our model differ from those provided by standard reinforcement learning models. Nevertheless, we show that an actor-critic model converges to the weights required for optimal decision making when biologically realistic limits on synaptic weights are introduced. We also describe the model's predictions concerning reaction times and neural responses during learning, and we discuss directions required for further integration of reinforcement learning and optimal decision-making theories.