ML/RL
What PPO + MCTS Taught Me About Strategy Learning
2026-02-02 · 8 min
A practical reflection on pairing policy learning and tree search, including where theory met implementation friction.
PPO and MCTS complement each other well, but only if you are explicit about what each one is responsible for. PPO can improve general policy quality over many games, while MCTS can sharpen local decision quality when a position demands deeper lookahead.
Most of the hard work was not in model architecture; it was in data and evaluation discipline. I needed reliable rollouts, clear metrics, and consistent baselines so improvements were real and not artifacts of noisy training runs.
The biggest lesson was to treat search and learning as a feedback loop rather than separate components. As training stabilized, MCTS traces became more informative, and as search quality improved, policy targets became more useful.