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README.md

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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**
 This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).