In policy gradient methods, the agent consists of two components: an actor and a critic. The actor represents the policy and is typically implemented using a neural network. It takes the agent’s observation as input and outputs a probability distribution over the action space. The critic estimates the value function of the state and usually has the same neural network architecture as the actor. However, it outputs a single real value that corresponds to the value of the state.

One mistake in the original text is that it says “implemented by a with a neural network” instead of “implemented using a neural network”.
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