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@@ -38,7 +38,7 @@ And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSi
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/thumbnail.jpg" alt="Unit 3 Thumbnail">
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In this notebook, **you'll train a Deep Q-Learning agent** playing Space Invaders using [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), a training framework based on [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) that provides scripts for training, evaluating agents, tuning arameters, plotting results and recording videos.
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In this notebook, **you'll train a Deep Q-Learning agent** playing Space Invaders using [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), a training framework based on [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) that provides scripts for training, evaluating agents, tuning parameters, plotting results and recording videos.
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We're using the [RL-Baselines-3 Zoo integration, a vanilla version of Deep Q-Learning](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) with no extensions such as Double-DQN, Dueling-DQN, and Prioritized Experience Replay.
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