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Update rl-framework.mdx

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Thomas Simonini
2022-12-05 17:56:20 +01:00
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units/en/unit1/rl-framework.mdx
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@@ -14,7 +14,7 @@ To understand the RL process, let’s imagine an agent learning to play a platfo
\$\sqrt{2}\$
- Our Agent receives **state $S_0$** from the **Environment** — we receive the first frame of our game (Environment).
- Our Agent receives **state \\(S_0\\)** from the **Environment** — we receive the first frame of our game (Environment).
- Based on that **state \\(S_0\\),** the Agent takes **action \\(A_0\\)** — our Agent will move to the right.
- Environment goes to a **new** **state \\(S_1\\)** — new frame.
- The environment gives some **reward \\(R_1\\)** to the Agent — we’re not dead *(Positive Reward +1)*.