Actor-Critic Methods
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What is Actor-Critic Methods?
Actor-Critic methods in reinforcement learning are a type of algorithm that combines the strengths of both value-based and policy-based approaches. They use two separate models: an "actor" that learns the optimal policy (how to act), and a "critic" that estimates the value function (how good a state or action is). The critic evaluates the actor's actions, providing feedback that helps the actor improve its policy, while the actor uses this feedback to refine its decision-making process.
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More in stage 26: Metrics to Evaluate
Actor-Critic Methods: common questions
Can I learn Actor-Critic Methods in Hindi?
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Is this Actor-Critic Methods tutorial for beginners?
It is lesson 107 of 150 in the free Machine Learning roadmap, in stage 26, Metrics to Evaluate. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.