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Update Actor Critic

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simoninithomas
2023-02-25 15:23:02 +01:00
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units/en/unit6/advantage-actor-critic.mdx
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@@ -16,7 +16,7 @@ On the other hand, your friend (Critic) will also update their way to provide fe
This is the idea behind Actor-Critic. We learn two function approximations:
- *A policy* that **controls how our agent acts**: \\( \pi_{\theta}(s,a) \\)
- *A policy* that **controls how our agent acts**: \\( \pi_{\theta}(s) \\)
- *A value function* to assist the policy update by measuring how good the action taken is: \\( \hat{q}_{w}(s,a) \\)
@@ -24,7 +24,7 @@ This is the idea behind Actor-Critic. We learn two function approximations:
Now that we have seen the Actor Critic's big picture, let's dive deeper to understand how Actor and Critic improve together during the training.
As we saw, with Actor-Critic methods, there are two function approximations (two neural networks):
- *Actor*, a **policy function** parameterized by theta: \\( \pi_{\theta}(s,a) \\)
- *Actor*, a **policy function** parameterized by theta: \\( \pi_{\theta}(s) \\)
- *Critic*, a **value function** parameterized by w: \\( \hat{q}_{w}(s,a) \\)
Let's see the training process to understand how Actor and Critic are optimized: