From 57ff8dba8969d2979ff2284e9bd4b3860d105999 Mon Sep 17 00:00:00 2001 From: Andrey Voroshilov Date: Sun, 8 Jan 2023 16:04:53 -0800 Subject: [PATCH] Using multiply operator instead of `x` in a formula --- units/en/unit3/from-q-to-dqn.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unit3/from-q-to-dqn.mdx b/units/en/unit3/from-q-to-dqn.mdx index 5b119c2..48e0eb6 100644 --- a/units/en/unit3/from-q-to-dqn.mdx +++ b/units/en/unit3/from-q-to-dqn.mdx @@ -18,7 +18,7 @@ Q-Learning worked well with small state space environments like: But think of what we're going to do today: we will train an agent to learn to play Space Invaders a more complex game, using the frames as input. -As **[Nikita Melkozerov mentioned](https://twitter.com/meln1k), Atari environments** have an observation space with a shape of (210, 160, 3)*, containing values ranging from 0 to 255 so that gives us \\(256^{210x160x3} = 256^{100800}\\) (for comparison, we have approximately \\(10^{80}\\) atoms in the observable universe). +As **[Nikita Melkozerov mentioned](https://twitter.com/meln1k), Atari environments** have an observation space with a shape of (210, 160, 3)*, containing values ranging from 0 to 255 so that gives us \\(256^{210 \times 160 \times 3} = 256^{100800}\\) (for comparison, we have approximately \\(10^{80}\\) atoms in the observable universe). * A single frame in Atari is composed of an image of 210x160 pixels. Given the images are in color (RGB), there are 3 channels. This is why the shape is (210, 160, 3). For each pixel, the value can go from 0 to 255.