PPO Deconstructed for the Pseudocode-Weary

What it does

Implements Proximal Policy Optimization from scratch in PyTorch for continuous control tasks. The code follows OpenAI’s Spinning Up pseudocode literally, with comments marking each algorithm step so you can map theory to implementation. It is explicitly a teaching tool: no fancy tricks, just a clean actor-critic network and a learn function designed to feel familiar if you have used stable_baselines.

The interesting bit

The author treats readability as a feature, not a bug. Every file has a single job — ppo.py for the learning logic, network.py for feed-forward nets, eval_policy.py for evaluation — and the pseudocode line numbers are annotated directly in the source. There is even a graph_code directory with the scaffolding to reproduce the Medium article’s benchmarks, though generating all the data takes roughly ten hours.

Key highlights

• Maps directly to OpenAI Spinning Up pseudocode; each algorithm step is labeled in ppo.py
• Clean separation of concerns: ppo.py, network.py, eval_policy.py, and graph_code/ each handle one thing
• Targets continuous Box observation and action spaces out of the box, with discrete modifications left as an exercise
• Includes data-collection scripts to regenerate the graphs from the companion Medium series

Caveats

• Only supports continuous Gym environments with Box spaces unless you modify the code yourself
• The author expects you to already know policy-gradient theory and PPO basics; this is not a from-scratch RL primer
• Hyperparameters are hardcoded in main.py rather than exposed as arguments, which the author admits is an intentional choice to keep commands short

Verdict

Grab this if you are an RL student who has read the PPO paper but gets lost in production frameworks. Skip it if you need a plug-and-play trainer or discrete-action support without editing source.