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Policy Gradient methods and Proximal Policy Optimization (PPO): diving into Deep RL!

In this episode I introduce Policy Gradient methods for Deep Reinforcement Learning.After a general overview, I dive into Proximal Policy Optimization: an algorithm designed at OpenAI that tries to find a balance between sample efficiency and code complexity. PPO is the algorithm used to train the OpenAI Five system and is also used in a wide range of other challenges like Atari and robotic control tasks.If you want to support this channel, here is my patreon link:--- You are amazing!! ;)Links mentioned in the video:⦁ PPO paper: TRPO paper: OpenAI PPO blogpost: Aurelien Geron: KL divergence and entropy in ML: Deep RL Bootcamp - Lecture 5: RL-adventure PyTorch implementation: OpenAI Baselines TensorFlow implementation:

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