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: https://patreon.com/ArxivInsights --- You are amazing!! ;) If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge Links mentioned in the video: ⦁ PPO paper: https://arxiv.org/abs/1707.06347 ⦁ TRPO paper: https://arxiv.org/abs/1502.05477 ⦁ OpenAI PPO blogpost: https://blog.openai.com/openai-baselines-ppo/ ⦁ Aurelien Geron: KL divergence and entropy in ML: https://youtu.be/ErfnhcEV1O8 ⦁ Deep RL Bootcamp - Lecture 5: https://youtu.be/xvRrgxcpaHY ⦁ RL-adventure PyTorch implementation: https://github.com/higgsfield/RL-Adventure-2 ⦁ OpenAI Baselines TensorFlow implementation: https://github.com/openai/baselines