Policy Optimization (PPO)

In 2018 OpenAI made a breakthrough in Deep Reinforcement Learning, this was possible only because of solid hardware architecture and using the state of the art's algorithm: Proximal Policy Optimization. The main idea of Proximal Policy Optimization is to avoid having too large a policy update. To do that, we use a ratio that tells us the difference between our new and old policy and clip this ratio from 0.8 to 1.2. Doing that will ensure that the policy update will not be too large. This tutorial will dive into understanding the PPO architecture and implement a Proximal Policy Optimization (PPO) agent that learns to play Pong-v0.