Portfolio Project

Nonogram Solver

Reinforcement Learning (RL)

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Data Science Machine Learning Python PyTorch

STAR Summary

Situation: I wanted to see if an RL agent could learn to solve Nonogram puzzles.
Task: Owned the end-to-end build, from implementation through the final deliverable.
Action: Generated large batches of 5x5 puzzles and trained a hybrid CNN + Transformer policy network to solve them.

Shaped rewards around unique guesses, row/column completions, and full-board solves to guide exploration.
Result: Evaluated on generated 5x5 boards, but the published materials do not yet include a reproducible held-out solve-rate result.
Stack: Python · PyTorch · AWS · Docker
Status: Live interactive demo

Evaluation & tradeoffs

Status: Not benchmarked
Goal: Measure whether the learned policy can complete unseen generated 5x5 Nonogram boards.
Dataset: Generated 5x5 Nonogram puzzles.
Split: The project references held-out boards, but split sizes, seeds, and a reproducible evaluation set are not published.
Baseline: No rule-based, search-based, or random-policy baseline is published.
Decision: Retained the hybrid CNN and Transformer policy for the live step-by-step solver demonstration.
Limitations: A held-out solve rate with board count, seed, and confidence interval has not been published.

Results on generated 5x5 boards do not establish performance on larger or human-authored puzzles.
Evidence: Project repository