Intro to Autonomous Robots
Actor-critic methods are a type of reinforcement learning algorithm that combines two components: the actor, which decides on the actions to take, and the critic, which evaluates the actions taken by providing feedback on their effectiveness. This dual approach allows for more efficient learning by separating the policy (the actor) from the value function (the critic), enabling better convergence and optimization in complex environments, especially in deep learning scenarios.
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