End-to-End PPO Training: Describe Your Pipeline

You are asked to explain, in concrete and reproducible terms, how you trained a policy with Proximal Policy Optimization (PPO) for a real-world machine learning engineering project.

Please cover the following, using specific design choices, numbers, and rationales:

Setup and Interfaces

1. Environment setup: simulation vs. real, physics/sensor fidelity, time limits.
2. Observation space: what features, dimensions, preprocessing/normalization.
3. Action space: discrete vs. continuous, ranges, squashing/scaling.

Learning Signal and Data Collection

4. Reward shaping: components, weights, potential-based shaping if applicable.
5. Rollout collection: parallelization, rollout length (horizon), total batch size per update.
6. Advantage estimation: GAE or alternatives; formulas and normalization.

Optimization and Stability

7. Key PPO hyperparameters: clip range, policy/value learning rates and schedules, epochs, minibatch size, entropy and value loss coefficients, gradient clipping, target KL, value clipping.
8. Normalization: observation normalization, reward/return normalization or PopArt.
9. Parallelization strategy: vectorized vs. distributed, CPU/GPU usage.
10. Checkpointing and early stopping: what, when, and how you save; early-stopping criteria.

Evaluation and Engineering Considerations

11. Evaluation protocol: offline metrics, validation environments/seeds, OOD tests, ablations.
12. Handling stability and sample efficiency: common pitfalls and fixes.
13. Sim-to-real transfer (if relevant): domain randomization, system ID, safety constraints, fine-tuning.

Be concise but specific; include concrete values (e.g., batch sizes, horizons, clip ranges) and justify trade‑offs.