Policy gradient is a type of reinforcement learning algorithm that optimizes the policy directly by adjusting the parameters of the policy network based on the rewards received from actions taken. Unlike value-based methods, which focus on estimating the value of actions, policy gradient methods learn a stochastic policy that maps states to actions, allowing for better exploration of action spaces. This technique is particularly beneficial in environments with large or continuous action spaces.
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