+A2C, or [Advantage Actor-Critic](/wiki/Advantage_Actor-Critic), is a prominent algorithm in [Reinforcement Learning](/wiki/Reinforcement_Learning). It refines the Actor-Critic approach by using an "advantage" function to more effectively guide an agent's learning toward optimal policies.
+## See also
+- [Actor-Critic](/wiki/Actor-Critic)
+- [Policy Gradient](/wiki/Policy_Gradient)
+- [Deep Learning](/wiki/Deep_Learning)
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