Decision-making for goal-directed actions via reinforcement learning (RL) is a fundamental component of complex behaviors. Central to RL theory is the balance between exploration and exploitation, which enables agents to interpret the environment using trial and error to learn an optimal strategy for maximizing reward. Determining the optimal parameters for when to switch between exploration/exploitation states in RL models has been difficult, and thus requires new biological insights. Recent work from our lab implicates locus coeruleus norepinephrine release (LC-NE) in signaling exploration and exploitation states. LC-NE neurons exhibit phasic activity in an RL task when presented with uncertain stimulus evidence to facilitate task execution/exploration, and after receiving a surprising reinforcement to facilitate task optimization/exploitation on the next trial. How these different phasic LC-NE signals are integrated in target regions to modulate different aspects of behavior is unknown. One possibility is through spatiotemporal integration by astrocytes, which are highly responsive to NE, are known to be involved in learning and memory, and can modulate neuronal activity on within-trial and between-trial timescales. Here, we propose that LC-NE release during an RL task causes changes in cortical network dynamics, facilitated through astrocyte signaling, that enable task execution and optimization. We will examine the effects of LC-NE and astrocytes on neuronal population dynamics and RL using innovative approaches combining dual 2-photon imaging of astrocytes and neurons in frontal/prefrontal cortex, high density neural recordings, optogenetic and chemogenetic manipulation of neurons and astrocytes, and computational approaches to define the effects of LC-NE and astrocytes on neuronal populations and task encoding. Finally, we will develop biologically informed computational models of astrocyte-neuron interactions during learned behavior. In Aim 1, we will record cortical astrocytes and neurons in mice performing our RL task. We will use high density single-unit recordings and population analyses to determine how population dynamics evolve during different task epochs. Using this information, we will determine how silencing LC-NE affects astrocyte and neuron computations and dynamics during RL. In Aim 2, we will use chemogenetic and optogenetic manipulations of astrocyte calcium to determine how astrocyte dynamics contribute to RL behaviors, and how this activity affects neuronal population dynamics. In Aim 3, we will examine the hypothesis that extending RL algorithms via NE- astrocyte signals can explain exploration at low stimulus evidence, and that NE-astrocyte interactions across trials would be reflected in policy gradient learning rules to promote exploitation. Finally, we will determine whether incorporating NE-astrocyte-neuron interactions into a recurrent neural network model can provide a rich model for behavior and identify circuit...