v1.0.0 — Final PPO Implementation and Experimental Analysis

Overview

v1.0.0 marks the first complete release of the PPO from scratch project.

The project implements Proximal Policy Optimization (PPO) in PyTorch and includes a full training pipeline, evaluation, logging, plotting, and benchmarking against Stable-Baselines3.

Key Features

• PPO implemenation from scratch
• actor-critic architecture
• Generalized Advantage Estimation (GAE)
• clipped PPO objective
• deterministic policy evaluation
• CSV logging and automated plotting
• structured experiment outputs
• benchmark against Stable-Baselines3

Results

• the custom PPO implementation successfully learns CartPole-v1
• evaluation rewards consistently reach ~150-250 with peaks up to ~450
• Stable-Baselines3 achieves perfect performance (500 reward, zero variance)
• results demonstrate correct learning behavior and meaningful policy improvement

Insights

• the custom PPO implementation is correct but less stable than a production-grad implementation
• performance differences are explained by:
  • a simpler training pipeline
  • fewer engineering optimizations
  • higher sensitivity to hyperparameters
  • remaining variance in policy learning

Conclusion

This project demonstrates a complete reinforcemenet learning workflow:

• algorithm implementation
• experimentation
• benchmarking
• and analysis

It provides a solid foundation for further reinforcement learning research and experimentation.

Future Work

• extend to more complex environments (e.g. LunarLander)
• improve training stability
• explore additional RL algorithms