Proximal Policy Optimization Implementation: 9 Atari-specific Details (2/3)
Proximal Policy Optimization (PPO) is one of the most popular reinforcement learning algorithms, and works with a variety of domains from robotics control to Atari games to chip design
In this video, we dive deep into 9 Atari-specific implementation details of PPO and build from the PPO implementation from our last video (https://youtu.be/MEt6rrxH8W4).
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Source code: https://github.com/vwxyzjn/ppo-implementation-details
Related blog post: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/
Background music: Flutes Will Chill — https://artlist.io/song/48722/flutes-wil…
Watch on YouTube ↗
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Chapters (18)
Introduction
1:00
Setup
1:55
Environment Preprocessing
2:23
1. NoopResetEnv
3:17
2. MaxAndSkipEnv
3:48
3. EpisodicLifeEnv
4:10
4. FireResetEnv
4:56
5. ClipRewardEnv
5:18
6. Image Transformation
5:49
7. FrameStack
6:29
8. Shared Nature-CNN network
8:02
9. Scale the input to [0, 1]
8:17
Match hyperparameters
8:40
Give it a run
9:04
Stream metrics live
9:13
Retrieve experiments done a year ago
11:24
Videos of agents playing the game
11:45
Summary of changes
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