From 9c1e655b97906b88cb47e0c63f0b9952861ede7e Mon Sep 17 00:00:00 2001 From: Antony Phillips Date: Sun, 18 Jun 2023 12:20:58 +0100 Subject: [PATCH] Update deep-q-network.mdx --- units/en/unit3/deep-q-network.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unit3/deep-q-network.mdx b/units/en/unit3/deep-q-network.mdx index dc8cb13..50cd4f2 100644 --- a/units/en/unit3/deep-q-network.mdx +++ b/units/en/unit3/deep-q-network.mdx @@ -32,7 +32,7 @@ That’s why, to capture temporal information, we stack four frames together. Then the stacked frames are processed by three convolutional layers. These layers **allow us to capture and exploit spatial relationships in images**. But also, because the frames are stacked together, **we can exploit some temporal properties across those frames**. -If you don't know what convolutional layers are, don't worry. You can check out [Lesson 4 of this free Deep Reinforcement Learning Course by Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188) +If you don't know what convolutional layers are, don't worry. You can check out [Lesson 4 of this free Deep Learning Course by Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188) Finally, we have a couple of fully connected layers that output a Q-value for each possible action at that state.