Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use LimTara/DeepQLearning-SpaceInvaders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use LimTara/DeepQLearning-SpaceInvaders with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="LimTara/DeepQLearning-SpaceInvaders", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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{'render_mode': 'rgb_array'}
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```
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#
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Finally, we now have to display our model so the audience can see what we've done! To do that, first you must install the following libraries and run a virtual screen:
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```%%capture
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!apt install python-opengl
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virtual_display.start()
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```
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Once you're in your model repository page, go over to "Files and Versions" and create a new folder called "replay.mp4" and simply upload your video and BOOM the virtual display will be right there in your model card! 🖥️
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{'render_mode': 'rgb_array'}
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```
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# Display 👀
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Finally, we now have to display our model so the audience can see what we've done! To do that, first you must install the following libraries and run a virtual screen:
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```%%capture
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!apt install python-opengl
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virtual_display.start()
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```
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Once you're in your model repository page, go over to "Files and Versions" and create a new folder called "replay.mp4" and simply upload your video and BOOM the virtual display will be right there in your model card! 🖥️
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# Saving your Changes 💾
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Oftentimes we are in the middle of coding a model, but suddenly we close the tab and everything is gone! Certainly, that is something that nobody wants to experience right?
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So in order to prevent that from happening, go over to the "file" on the top left and click "Save a copy in Drive". Once you've done that, the changes you make will be saved even when you close your tab. Now you can freely edit your code without worrying that your precious hard work will disappear! Phew!
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# Next Steps 🎉
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Congratulations! You've now completed the beginner's guide to creating a Deep Q-Learning model! Now it's your turn, try and create your own model and remember that errors are bound to happen so be patient and have fun exploring! 🤩
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