repo_id stringlengths 15 86 | file_path stringlengths 28 180 | content stringlengths 1 1.75M | __index_level_0__ int64 0 0 |
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hf_public_repos/deep-rl-class/units/en | hf_public_repos/deep-rl-class/units/en/unit5/curiosity.mdx | # (Optional) What is Curiosity in Deep Reinforcement Learning?
This is an (optional) introduction to Curiosity. If you want to learn more, you can read two additional articles where we dive into the mathematical details:
- [Curiosity-Driven Learning through Next State Prediction](https://medium.com/data-from-the-tren... | 0 |
hf_public_repos/deep-rl-class/units/en | hf_public_repos/deep-rl-class/units/en/unit5/snowball-target.mdx | # The SnowballTarget Environment
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif" alt="SnowballTarget"/>
SnowballTarget is an environment we created at Hugging Face using assets from [Kay Lousberg](https://kaylousberg.com/). We have an option... | 0 |
hf_public_repos/deep-rl-class/units/en | hf_public_repos/deep-rl-class/units/en/unit5/conclusion.mdx | # Conclusion
Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳.
The best way to learn is to **practice and try stuff**. Why not try another environment? [ML-Agents has 18 different environments](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learn... | 0 |
hf_public_repos/deep-rl-class/units/en | hf_public_repos/deep-rl-class/units/en/unit5/pyramids.mdx | # The Pyramid environment
The goal in this environment is to train our agent to **get the gold brick on the top of the Pyramid. To do that, it needs to press a button to spawn a Pyramid, navigate to the Pyramid, knock it over, and move to the gold brick at the top**.
<img src="https://huggingface.co/datasets/huggingf... | 0 |
hf_public_repos/deep-rl-class/units/en | hf_public_repos/deep-rl-class/units/en/live1/live1.mdx | # Live 1: How the course work, Q&A, and playing with Huggy
In this first live stream, we explained how the course work (scope, units, challenges, and more) and answered your questions.
And finally, we saw some LunarLander agents you've trained and play with your Huggies 🐶
<Youtube id="JeJIswxyrsM" />
To know when ... | 0 |
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