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 for specific tasks using physically simulated bodies and environments. However%2C the utility of these methods goes beyond the constraints of a specific task%3B they offer an exciting framework for understanding the organization of an animal sensorimotor system in connection to its morphology and physical interaction with the environment%2C as well as for deriving general design rules for sensing and actuation in robotic systems. Algorithms and code implementing both learning agents and environments are increasingly available%2C but the basic assumptions and choices that go into the formulation of an embodied feedback control problem using deep reinforcement learning may not be immediately apparent. Here%2C we present a concise exposition of the mathematical and algorithmic aspects of model-free reinforcement learning%2C specifically through the use of textit{actor-critic} methods%2C as a tool for investigating the feedback control underlying animal and robotic behavior.?quality=80&w=800)
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