by HuggingFace 🤗 will teach about Deep Reinforcement Learning from beginner to expert:
"LunarLander" agent that will learn to land correctly on the Moon using PPO architecture and MLpPolicy and the trained agent is uploaded into the Hugging Face Hub. LunarLander ---> Result
"Huggy" the Dog to fetch the stick and then play with him directly in your browser
Result:Website
"FrozenLake-v1" where our agent will need to go from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoiding holes (H). FrozenLake-v1 --->Result
"Taxi-v3" will need to learn to navigate a city to transport its passengers from point A to point B. Taxi-v3---> Result
"Deep Q-Learning with Atari Games using RL Baselines3 Zoo" This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo.Deep Q-Learning with Atari Games using RL Baselines3 Zoo--->Result
"Cartpole and PixelCopter" In this notebook,coded first Deep Reinforcement Learning algorithm from scratch: Reinforce also called Monte Carlo Policy Gradient. Code your first Deep Reinforcement Learning Algorithm And test its robustness.
--->Cartpole Result
"ML Agents: Snownball Target and Pyramids Training" In this notebook, The first one will learn to shoot snowballs onto spawning targets and The second need to press a button to spawn a pyramid, then navigate to the pyramid, knock it over,and move to the gold brick at the top. Introduction to UNITY MLAgents
"Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet and Panda-Gym" In this notebook, trained A2C agent using Stable-Baselines3 in robotic environments. And train two robots: A spider to learn to move and A robotic arm to move in the correct position Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet and Panda-Gym