Policy_gradient_method?useskin=vector

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Policy gradient methods are a class of reinforcement learning algorithms and a sub-class of policy optimization methods. Unlike value-based methods which learn a value function to derive a policy, policy optimization methods directly learn a policy function that selects actions without consulting a value function. For policy gradient to apply, the policy function is parameterized by a differentiable parameter .[1] In policy-based RL, the actor is a parameterized policy function , where are the parameters of the actor. The actor takes as argument the state of the environment and produces a…

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• Rishi Kothari

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