Adaptive Python Games

Python practice evolved into a small online-learning experiment. The original Rock–Paper–Scissors script is preserved unchanged; adaptive_rps.py adds an opponent model that learns move frequencies during play and selects a counter-strategy.

Run

python adaptive_rps.py
python -m unittest discover -s tests

AI/ML connection

The adaptive player is an interpretable statistical baseline, not a trained neural model. It demonstrates the feedback loop behind online learning:

observe move -> update history -> estimate behavior -> choose action -> repeat

Possible future experiments include Markov sequence models, contextual bandits, reinforcement learning, and strategy evaluation over simulated tournaments.

Original work

Rock Paper Scissors Game contains the first interactive implementation and remains in the repository as a learning milestone.