George Hotz | Programming | RL is dumb and doesn't work | Reinforcement Learning LunarLander Part 2
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 Published On Jan 10, 2024

Date of the stream 7 Jan 2024.
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Live-stream chat added as Subtitles/CC - English (Twitch Chat) - at the bottom - Show Transcript

Sources:
- https://github.com/geohot/dumbrl
- https://stable-baselines3.readthedocs...
-    • Deadliest Journeys - Congo: The Last ...   (Deadliest Journeys - Congo: The Last Train in Katanga)
- https://andyljones.com/posts/rl-debug...
- https://spinningup.openai.com/en/latest/
- https://arxiv.org/pdf/1912.02875.pdf (Reinforcement Learning Upside Down)
tinygrad bounties:
- https://docs.google.com/spreadsheets/...
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Chapters:
00:00:00 intro
00:01:40 stream disclaimer, twitch ban
00:02:45 only 50% of subscription money
00:03:05 kick.com streaming
00:04:45 kick reach out to George, twitch issues
00:06:20 drugs banner, legal in california
00:08:05 50% money to twitch too much, twitch remove the banner
00:09:20 hyubsama food stream, twitch banned users
00:12:00 streaming on X, negotiating power
00:14:40 stream statistics, streaming schedule
00:16:10 applying for twitch partner
00:17:30 twitch revenue
00:18:30 perplexity best way to get banned on twitch
00:22:50 andrew tate impression
00:23:50 stable baselines 3
00:29:40 np.random.randint
00:32:44 NoneType object does not support item assignment
00:33:00 perplexity
00:35:40 render mode defined human
00:37:20 good play, size=10
00:40:30 stable baselines 3 just works
00:50:00 passed a tuple, array element with a sequence
00:51:15 learning
00:52:50 decision transformer stable baselines 3
00:55:20 github.com/geohot/dumbrl
00:56:30 cartpole, stable baselines decision transformer
00:59:30 Jax, wrapper for vectorized environments
01:00:30 deadliest journeys congo, ancestor pothole
01:01:11 building infrastructure, fixing the road
01:01:20 bugs, carefully building infrastructure, CI testing
01:04:00 README
01:05:40 deleting a lot of tinygrad, focusing on what needs to work well
01:09:55 decision transformer repo
01:13:10 beautiful_cartpole.py
01:20:07 andy jones debugging rl
01:24:00 if you are following along
01:29:00 the problem are bugs
01:32:00 asking perplexity, openai spining up and deep rl
01:36:25 log_softmax
01:39:00 broadcasting bug, 2, 3, 5
01:47:20 no detach(), ppo, exp
02:02:00 why is my ppo not working
02:07:40 fast cartpole
02:11:50 banned user
02:15:50 asking it to learn
02:17:15 hyper parameter land
02:25:45 lucky
02:27:30 !!!LOUD WARNING!!! why it's not solving
02:33:20 3 layer network
02:41:30 value function
02:42:50 writing pytorch
02:48:10 if it works in pytorch shutting down tiny corp
02:52:50 pytorch numeric stability
02:55:10 frustrating, having faith in tiny grad
02:56:00 very easy to make progress in tiny grad
02:57:18 tiny grad more numerically stable
03:04:00 the most dead simple thing
03:13:30 size 2, 3 solving
03:14:35 going even simpler
03:19:00 batch size = 4
03:22:40 reward broken
03:27:10 it becomes like an identity matrix over time
03:28:40 this is fire, the gradient, single weight matrix
03:33:00 so beautiful, love watching deep learning happen
03:44:10 learning rate too high
03:51:00 that one does not learn
03:52:40 dying relu, 0xnan getting VIP
04:00:40 advantage
04:06:40 Alex on the phone
04:08:45 no clips, taking out of context
04:11:00 value funtion all noise
04:18:10 graph go up
04:24:00 messing with hyperparameters randomly
04:26:10 slow graph drawing
04:28:20 sampling bias
04:29:10 lower discount factor, larger replay buffer
04:32:45 no major bugs, ppo major bug
04:36:20 entropy loss
04:38:40 counter intuitive in deep learning, bigger learn better
04:40:40 overheads
04:41:40 one good landing
04:42:55 50, 51
04:44:10 Alex home
04:46:30 send this video to a doomer
04:48:00 good enough landing
04:50:40 expectations too high
04:51:35 twitch won't contact George
04:53:30 hope, upside down rl, juergen schmidhuber
04:54:10 good reliable solution to everything
04:54:40 Alex, no checkpoints
04:55:10 last landing, end of the episode
04:55:30 thank you for watching

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