Policy gradient methods utilize an agent that consists of two main components, namely an actor and a critic. The actor is responsible for representing the policy and is commonly implemented using a neural network. It takes the agent's observation as input and produces a probability distribution over the action space. The critic, on the other hand, is responsible for estimating the value function of the state. It typically has the same neural network architecture as the actor, but its output is a single real value that corresponds to the value of the state.

As for mistakes there is a minor typo in the original text: "usually implemented by a with a neural network" should be "usually implemented with a neural network." Other that that, the original text is clear and understandable
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