Actor-critic is a type of reinforcement learning algorithm that uses two models: the actor, which proposes actions based on the current policy, and the critic, which evaluates those actions and provides feedback on their quality. This approach combines the benefits of both policy-based and value-based methods, allowing for more stable and efficient learning. The actor-critic framework effectively integrates exploration and exploitation strategies to improve decision-making in complex environments.
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