Elements of Reinforcement Learning

Elements of Reinforcement Learning
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[1] Reinforcement Learning book: incompleteideas.net/book/RLboo...
[2] Paradigms of ML: idapgroup.com/blog/types-of-m...
[3] Pong: • DQN Breakout
[4] Learning to walk: • Emergence of Locomotio...
[5] ChatGPT blog: openai.com/blog/chatgpt
[6] Chess: www.kaggle.com/code/arjanso/r...
[7] Model Free vs Model Based RL: spinningup.openai.com/en/late...
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[@CodeEmporium]

If you think I deserve it, please give this video a like as it will help circulate the video immensely. Thank you so much for the support so far !

[@pj-nz6nm]

Please make more videos on reinforcement learning,my knowledge about this field is very poor.

[@CodeEmporium]

I definitely shall make more videos! Thanks for the comment !

[@sloth_in_socks]

Great video! It's funny you mentioned unsupervised learning at the start but didn't mention LLMs

[@minlingg91]

keep up the good work! im currently doing a traineeship in AI and your videos have been immensely helpful.

[@amiralioghli8622]

Thank you, sir, for sharing valuable information through your UA-cam channel. Once again, I have a request: please create a series on how to apply Transformers to time series tasks such as anomaly detection, forecasting, or classification. Working on just one of these tasks would be sufficient for us. I have followed numerous articles, short notes, and videos regarding the application of Transformers to time series data, but it is still not clear to me. I am a beginner on this Transformer journey, and there are no useful videos available on UA-cam overall.

[@casualpasser-by5954]

Very nice, short and clear overview of reinforcement learning! However, in the end of the video, I think, the distinction between model-free and model-based algorithms wasn't explained well. It is not about does one train an algorithm on the simulatied or real-world data. Is real world the source of the information or it is a simulation - from the algorithmic point of view the information is in both cases just numbers, produced by some external environment. The real difference between model-free and model-based is that model-based algorithms have intrinsic model within them, which is adjusted during the training to better predict the behaviour of the environment. Of there is no such trainable model within an algorithm and we have only fixed external simulation - we still follow model-free approach.
Sorry for my English.

[@CodeEmporium]

Thanks for this! Honestly I think I am in agreement and this goes to show maybe the words I used in the end to describe this is confusing. Perhaps with this definition , a more concrete example would have been helpful like I had given the others. But I treated that last piece more like a footnote. I’ll probably dedicate more videos and time to this :)

[@slitihela1860]

can you prepare a video for Double Q-Learning Network
and Dueling Double Q-Learning Network
please

[@mdbayazid6837]

I would request for a book reading camp if possible

[@CodeEmporium]

Ooo this is a fun topic! I shall consider

[@arunima29]

Please make detailed videos on all the concepts of RL.

[@CodeEmporium]

Roger! I shall!

[@MilesBellas]

1.7x speed = best