🐢
Research Index

Join the Menttor community

Access accelerated AI inference, track progress, and collaborate on roadmaps with students worldwide.

🐢
Research Decoded/Schulman et al. (2017)

PPO: Proximal Policy Optimization

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.

Read Original Paper
PPO: Proximal Policy Optimization

In 2017, the Proximal Policy Optimization (PPO) paper from OpenAI introduced a reinforcement learning algorithm that balanced ease of implementation, sample efficiency, and ease of tuning. Before PPO, policy gradient methods were often sensitive to hyperparameter choices and could suffer from large, destructive weight updates. The researchers proposed a new objective function that constrains the change in the model's behavior during each step of learning. It was a shift toward making reinforcement learning as reliable and predictable as standard supervised learning.

Clipped Surrogate Objective

Clipped Surrogate Objective

The clipped surrogate objective function ensures that policy updates remain within a safe range.

The technical shift was the introduction of a 'clipped' surrogate objective. Instead of allowing the policy to change drastically based on a single piece of feedback, the algorithm limits the impact of updates that move too far away from the current behavior. As the paper states, 'This objective penalizes changes to the policy that move the probability ratio far from 1.' By clipping the incentive to make large changes, the model maintains stability even in complex environments. It is a method that prioritizes steady, incremental progress over erratic leaps in performance.

Sample Efficiency

The reasoning behind PPO was the need for an algorithm that could learn effectively from fewer interactions with the environment. By allowing for multiple epochs of gradient descent on the same batch of data, PPO achieved better sample efficiency than previous methods like TRPO. This revealed that the stability of the update process is a key factor in how quickly a model can learn. It suggested that in reinforcement learning, the quality of the update is often more important than the quantity of the data.

The Reliability Shift

The success of PPO led to its widespread adoption as the default reinforcement learning algorithm at many AI labs. It proved that complex robotic control and strategic game-playing could be achieved with an algorithm that is relatively simple to implement. This accessibility has fueled progress in many areas of AI, raising questions about whether the future of the field lies in increasingly complex mathematical models or in finding more robust ways to optimize the models we already have.

Dive Deeper