ស្វែងយល់ពី Proximal Policy Optimization | PPO | Machine Learning Series | TFD Workshop скачать в хорошем качестве

ស្វែងយល់ពី Proximal Policy Optimization | PPO | Machine Learning Series | TFD Workshop

វីដេអូដែលបាន Record នៃសិក្ខាសាលា Online អំពី "ស្វែងយល់ពី Proximal Policy Optimization" ជាផ្នែកនៃ Machine Learning Series Recorded video of online workshop: "Understanding Proximal Policy Optimization" as part of Web Security Series ចូលទាញយក Demo នឹង លំហាត់: https://github.com/tfd-ed/tfd-worksho... TFD Workshop Repo: https://github.com/tfd-ed/tfd-workshop 🔑 អ្វីដែលរៀនបាន Part 1: Reinforcement Learning Foundations The RL framework: agents, environments, rewards, and policies States, observations, and action spaces (discrete vs continuous) The credit assignment problem and why RL is challenging Real-world RL applications (games, robotics, control systems) Part 2: Policy Gradient Methods From value-based to policy-based methods Understanding the policy gradient theorem Why vanilla policy gradients are unstable The importance of trust regions in learning Part 3: Understanding PPO The fundamental problem PPO solves Clipping mechanism and surrogate objectives Actor-Critic architecture Generalized Advantage Estimation (GAE) Part 4: Complete PPO Implementation Actor and Critic neural networks in PyTorch Memory buffer for experience collection Computing advantages and returns The PPO update loop with clipping Part 5: Training the Lunar Lander Environment setup with Gymnasium Hyperparameter configuration Training loop implementation Monitoring and debugging training metrics Visualizing learned behaviors Live Demonstrations Lunar Lander Environment - Understanding the observation space and actions Untrained Agent Behavior - Random actions and crashes PPO Training Process - Watching the agent learn in real-time Trained Agent Performance - Successful landings and optimal behavior Training Metrics Visualization - Interpreting reward curves and losses Hands-On Lab Exercises Exercise 1: Understanding the environment and action space Exercise 2: Implementing the Actor-Critic networks Exercise 3: Computing advantages with GAE Exercise 4: The PPO update step Exercise 5: Training your own agent IG:   / darachaukh   YouTube:    / @tfdevs   Website: https://www.tfdevs.com/ Linkedin:   / qiang-cun-zhi   TikTok: https://www.tiktok.com/@chaudarakh?_r... Telegram Channel: https://t.me/tfdTech Facebook Page:   / chaudarascienceengineer   #MachineLearning #ReinforcementLearning #AI #PPO #Workshop #TechEducation #LearningByDoing #AIWorkshop #DeepLearning #PyTorch