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.

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Links mentioned in the video:
⦁ PPO paper: [ Ссылка ]
⦁ TRPO paper: [ Ссылка ]
⦁ OpenAI PPO blogpost: [ Ссылка ]
⦁ Aurelien Geron: KL divergence and entropy in ML: [ Ссылка ]
⦁ Deep RL Bootcamp - Lecture 5: [ Ссылка ]
⦁ RL-adventure PyTorch implementation: [ Ссылка ]
⦁ OpenAI Baselines TensorFlow implementation: [ Ссылка ]

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