Proximal Policy Optimization (PPO) is an advanced reinforcement learning algorithm that falls under the category of policy gradient methods. Developed by OpenAI, PPO is designed to improve the stability and reliability of training complex models by optimizing a policy in a way that ensures updates are not too large, thus maintaining a balance between exploration and exploitation. It achieves this by using a surrogate objective function that penalizes deviations from the previous policy beyond a certain threshold, effectively constraining the policy update to be within a "proximal" range. PPO is widely used in training large-scale models due to its simplicity, efficiency, and ability to handle high-dimensional action spaces, making it a popular choice in the field of Generative AI and robotics.
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