From 5313c300964faebfb3c0fadcde123f31427dc7da Mon Sep 17 00:00:00 2001 From: simoninithomas Date: Sat, 3 Dec 2022 15:01:58 +0100 Subject: [PATCH] Update Deep Q-Learning unit --- units/en/unit3/deep-q-algorithm.mdx | 2 +- units/en/unit3/hands-on.mdx | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/units/en/unit3/deep-q-algorithm.mdx b/units/en/unit3/deep-q-algorithm.mdx index cc6889a..d8dd604 100644 --- a/units/en/unit3/deep-q-algorithm.mdx +++ b/units/en/unit3/deep-q-algorithm.mdx @@ -99,4 +99,4 @@ The solution is: when we compute the Q target, we use two networks to decouple t Therefore, Double DQN helps us reduce the overestimation of q values and, as a consequence, helps us train faster and have more stable learning. -Since these three improvements in Deep Q-Learning, many have been added such as Prioritized Experience Replay, Dueling Deep Q-Learning. They’re out of the scope of this course but if you’re interested, check the links we put in the reading list. TODO Add reading list +Since these three improvements in Deep Q-Learning, many have been added such as Prioritized Experience Replay, Dueling Deep Q-Learning. They’re out of the scope of this course but if you’re interested, check the links we put in the reading list. diff --git a/units/en/unit3/hands-on.mdx b/units/en/unit3/hands-on.mdx index ef7c709..51a5b7e 100644 --- a/units/en/unit3/hands-on.mdx +++ b/units/en/unit3/hands-on.mdx @@ -1 +1,8 @@ # Hands-on [[hands-on]] + +Now that you've studied the theory behind Deep Q-Learning, **you’re ready to train your Deep Q-Learning agent to play Atari Games**. We'll start with Space Invaders, but you'll be able to use any Atari game you want 🔥 + +Environments + + +We're using the [RL-Baselines-3 Zoo integration](https://github.com/DLR-RM/rl-baselines3-zoo), a vanilla version of Deep Q-Learning with no extensions such as Double-DQN, Dueling-DQN, and Prioritized Experience Replay.