Proximal Policy Optimization
A detailed implementation of a popular reinforcement learning algorithm
Project Overview
In this project, I implement a popular deep reinforcement learning algorithm known as Proximal Policy Optimization (PPO). This implementation really gets into the details, diving deep into the mathematics and engineering practices that make PPO so effective.
I demonstrate how PPO allows agents to learn tasks ranging from simple environments like balancing a pole on a cart to more complex Atari games like Breakout, using only raw pixels as input.
Key Features:
- Complete implementation of PPO from scratch
- Support for both discrete and continuous action spaces
- Parallel environment sampling for efficiency
- CNN architecture for pixel-based learning
- Detailed training metrics and visualization
What is PPO?
Proximal Policy Optimization (PPO) is a reinforcement learning algorithm developed by OpenAI in 2017. It has become popular due to its stability, simplicity, and effectiveness across a wide range of tasks.
Unlike older policy gradient methods, PPO uses a "clipped" objective function that limits how much the policy can change in a single update, leading to more stable learning.
Learning Progress
One of the most fascinating aspects of reinforcement learning is watching an agent progress from random actions to skilled behavior through trial and error. In this project, you'll see how agents start with no knowledge and gradually master their environments through direct video captures of training.