Basic policy gradient methods, like REINFORCE, often suffer from high variance in their gradient estimates, leading to slow or unstable learning. This chapter introduces Actor-Critic methods, a family of algorithms designed to address this limitation. The core idea is to maintain two components: an actor that learns the policy π(a∣s) and a critic that learns a value function (like V(s) or Q(s,a)) to evaluate the actor's actions and provide lower-variance gradient signals.
You will study several key advancements building on this framework:
By the end of this chapter, you will understand the theory behind these advanced algorithms and be prepared to implement them for solving more complex reinforcement learning problems.
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