Policy Gradient Methods
Policy Gradient Methods are a class of reinforcement learning algorithms that optimize the parameters of a policy function to maximize the expected cumulative reward. Unlike value-based methods, which estimate the optimal value function and derive the policy from it, policy gradient methods directly optimize the policy function. This is done by computing the gradient of the expected cumulative reward with respect to the policy parameters and updating them using stochastic gradient ascent. Policy gradient methods can handle continuous action spaces and are well-suited for problems with high-dimensional state spaces. They can also incorporate prior knowledge or constraints into the optimization process, making them more flexible than value-based methods.
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