Pruning
Machine Learning
A policy gradient method that constrains policy updates to prevent destructively large changes.
Detailed Explanation
Proximal Policy Optimization (PPO) is an advanced reinforcement learning algorithm that improves policy stability by restricting the extent of policy updates. It uses a clipped objective function to ensure changes are neither too large nor too small, balancing exploration and exploitation. This method enhances learning efficiency and reliability, making it popular for training complex agents in dynamic environments.
Use Cases
- •Optimizes robotic navigation policies to ensure safe, reliable movements in unpredictable environments with minimal risk of unpredictable behavior.
Related Terms
Other terms in the Machine Learning category