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The primary advantage of PG methods is their ability to handle continuous action spaces—essential for robotic control and physical emulation—where Value-based methods struggle due to the "curse of dimensionality" in maximizing a discrete function over continuous inputs. This essay examines the progression from the seminal stochastic REINFORCE model to the deterministic DDPG model.