Intermediate · Reinforcement Learning
Proximal policy optimization (PPO)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A reinforcement learning algorithm that trains agents by making small, consistent updates to their decision policy.
Technical Definition
A reinforcement learning algorithm that trains agents by making small, consistent updates to their decision policy.
How it works
Proximal Policy Optimization (PPO) is a popular reinforcement learning algorithm. It is designed to train agents by optimizing their policy, which is the function that dictates their actions. PPO focuses on making incremental updates to the policy, which helps to stabilize training and prevent drastic changes that could lead to poor performance.
Related Concepts
- Reinforcement Learning — A paradigm where an agent learns to make decisions by receiving rewards or penalties from its environment through trial and error.