OpenAI's Vice President of Product, Peter Welinder, recently published an article on X, stating, "Everyone is researching Q-learning, but they will be surprised when they hear about Proximal Policy Optimization (PPO)." What is PPO? PPO is a reinforcement learning algorithm used to train artificial intelligence models to make decisions in complex or simulated environments. Interestingly, PPO became OpenAI's default reinforcement learning algorithm in 2017 due to its ease of use and high performance. The term "proximal" in PPO refers to the constraints applied to policy updates. These constraints help prevent significant changes in the policy, leading to more stable and reliable learning. OpenAI uses PPO because it is highly effective in optimizing sequential decision-making tasks. Furthermore, PPO strikes a balance between exploration and exploitation, which is crucial in reinforcement learning. It achieves this by gradually updating the policy while ensuring the changes are constrained. OpenAI adopts PPO in various use cases, ranging from training agents in simulated environments to mastering complex games. The versatility of PPO allows it to excel in scenarios where intelligent agents must learn a series of actions to achieve specific goals, making it valuable in fields such as robotics, autonomous systems, and algorithmic trading. It is highly likely that OpenAI intends to achieve AGI through games and simulated environments, leveraging PPO. Interestingly, earlier this year, OpenAI acquired Global Illumination to train agents in simulated environments.