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@@ -32,7 +32,7 @@ This course is **self-paced** you can start when you want 🥳.
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity) | [Towards better explorations methods with Curiosity](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity)| |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Advantage Actor Critic (A2C)](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Train a bipedal walker and a spider to learn to walk using A2C](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch) | [Proximal Policy Optimization (PPO)](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch) | [Code a PPO agent from scratch using PyTorch and bulletproof it with Classical Control Environments](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit8/unit8.ipynb) |
| TBA | Decision Transformers and offline Reinforcement Learning | 🏗️ |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit9#unit-9-decision-transformers-and-offline-reinforcement-learning-) | [Decision Transformers and offline Reinforcement Learning](https://github.com/huggingface/deep-rl-class/tree/main/unit9#unit-9-decision-transformers-and-offline-reinforcement-learning-) | [Train your first Offline Decision Transformer model from scratch to make a half-cheetah run](ADD LINK) |