DQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained
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In this video, I cover the paper that started the hype for deep RL - Playing Atari with deep RL, which introduced the DQN or deep Q-network.
You'll learn about:
✔️ All of the nitty-gritty details behind the paper
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✅ Paper: https://arxiv.org/abs/1312.5602
✅ A decent gist: https://github.com/higgsfield/RL-Adventure/blob/master/1.dqn.ipynb
Note: there is an error with the target Q-function he didn't freeze it but it's good enough - until I implement something myself :…
Watch on YouTube ↗
(saves to browser)
Chapters (13)
1:05
High-level overview of the paper
7:00
Experience replay buffer
10:35
Difficulties with RL (correlations, non-stationary distributions)
13:30
DQN is very general
17:00
MDP formalism and optimal Q function
22:37
Function approximators
23:50
The loss function explained
28:40
The deadly triad
30:55
Algorithm walk-through
35:00
Preprocessing and architecture details
37:20
Additional details - normalizing score, schedule, etc.
42:50
Agent training metrics
47:15
Results
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