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--- |
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library_name: stable-baselines3 |
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tags: |
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- LunarLander-v2 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: PPO |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: LunarLander-v2 |
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type: LunarLander-v2 |
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metrics: |
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- type: mean_reward |
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value: 260.63 +/- 20.34 |
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name: mean_reward |
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verified: false |
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--- |
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Today I landed my Lunar Lander on the moon using reinforcement learning 🌙 |
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Reinforcement Learning is a computational approach of learning from actions. |
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Instead of manually controlling the inputs and directions of the thrusters, I used a Proximal Policy Optimization agent that is a mix of value-based RL and policy-based RL to predict actions leading to the greatest sum of rewards. https://huggingface.co/AGI-CEO/ppo-LunarLander-v2 |
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I am learning this as part of Hugging Face's Deep RL Course (https://huggingface.co/learn/deep-rl-course/). It's a free, self-paced course. |
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I am planning on hosting group study sessions and create a free, open cohort to learn Deep Learning over the next 10 weeks. |
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I will host these study sessions and discussions on my AI Engineering / Entrepreneurship Discord group with 500+ members. |
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If you have some experience with Python and are interested in joining a group to learn Deep RL free of charge, please reach out to me. I am planning the first group session next Thursday at 10AM Eastern. |
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# **PPO** Agent playing **LunarLander-v2** |
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This is a trained model of a **PPO** agent playing **LunarLander-v2** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage (with Stable-baselines3) |
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TODO: Add your code |
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```python |
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from stable_baselines3 import ... |
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from huggingface_sb3 import load_from_hub |
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... |
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``` |
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