coorl-lost-cities/AGENTS.md

AGENTS.md

This repository is managed with uv. Use uv run ... for commands so the project environment and Cython extensions are built/loaded consistently.

Project Layout

• src/coolrl_lost_cities/games/classic/game.pyx: Cython Lost Cities engine.
• src/coolrl_lost_cities/games/classic/deep_cfr/: Deep CFR training, traversal, evaluation, analysis, and CLI code.
• configs/deep_cfr/: Deep CFR YAML configs.
• runs/: generated training runs. This path is gitignored and may be a symlink to larger storage.
• docs/: profiling notes, migration notes, and experiment documentation.

Core Commands

Run lint:

uv run ruff check .

Run all tests:

uv run pytest -q

Run focused Deep CFR tests:

uv run pytest -q tests/games/classic/test_deep_cfr_trainer.py

Run the CLI through the console script:

uv run lost-cities-deep-cfr --help

Equivalent module form:

uv run python -m coolrl_lost_cities.games.classic.deep_cfr.cli --help

Deep CFR Training

Main full config:

uv run lost-cities-deep-cfr train \
  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml

Unbounded config:

uv run lost-cities-deep-cfr train \
  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml

Short fixed-iteration run:

uv run lost-cities-deep-cfr train \
  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
  --iterations 100 \
  --save-latest-only

Use explicit run directories for experiments. Put Deep CFR runs under runs/deep_cfr/ and prefix generated run names with the date:

RUN_DIR="runs/deep_cfr/$(date +%Y-%m-%d_%H%M%S)_deep_cfr_experiment_name"
uv run lost-cities-deep-cfr train \
  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
  --checkpoint-dir "$RUN_DIR" \
  --max-iterations 100

Date-prefixed examples:

• runs/deep_cfr/YYYY-MM-DD_HHMMSS_deep_cfr_100iter
• runs/deep_cfr/YYYY-MM-DD_HHMMSS_deep_cfr_unbounded

Useful train overrides:

• --resume: resume from <checkpoint-dir>/latest.pt.
• --resume PATH: resume from a specific checkpoint.
• --exact-resume: require checkpoint config compatibility for exact resume.
• --no-save: disable checkpoint writes.
• --save-latest-only: keep only latest.pt.
• --save-iteration-interval N: archive every N iterations.
• --set PATH=VALUE: override arbitrary config fields, e.g. --set traversal.num_workers=4.

Long Runs

Run long jobs in a real tmux session so the user can attach and stop them. Do not rely on Codex command sessions for long user-observable training runs.

Start a long unbounded run:

tmux new-session -s coolrl-deepcfr-unbounded \
  -c /home/coolguy/dev/coolrl-lost-cities \
  'uv run lost-cities-deep-cfr train \
    --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml'

Attach later:

tmux attach -t coolrl-deepcfr-unbounded

Detach without stopping:

Ctrl+B, D

Stop training:

Ctrl+C

Follow logs from another terminal:

tail -f runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded/train.log

The unbounded config intentionally has:

run:
  iterations: null
  max_iterations: null
  max_hours: null
checkpoint:
  save_iteration_interval: 100

latest.pt is updated continuously; archive checkpoints are written every 100 iterations. If disk is tight, prefer --save-latest-only or increase save_iteration_interval.

Evaluation And Analysis

Evaluate a checkpoint:

uv run lost-cities-deep-cfr eval \
  --checkpoint runs/deep_cfr/<run-name>/latest.pt \
  --opponent random \
  --games 100 \
  --device cpu

Save evaluation game records:

uv run lost-cities-deep-cfr eval \
  --checkpoint runs/deep_cfr/<run-name>/latest.pt \
  --opponent random \
  --games 100 \
  --device cpu \
  --save-games runs/deep_cfr/<run-name>/eval_random_games.json

Generate analysis plots from metrics.jsonl:

uv run lost-cities-deep-cfr analyze \
  --run runs/deep_cfr/<run-name>

Write plots to a separate directory:

uv run lost-cities-deep-cfr analyze \
  --run runs/deep_cfr/<run-name> \
  --output-dir runs/deep_cfr/<run-name>/analysis

The analyzer reads metrics.jsonl and writes PNG files grouped by diagnostic section. Opponents are compared within each plot using fixed colors. The lost-cities-deep-cfr analyze subcommand uses the analyzer default smoothing window, currently 1 iteration (no smoothing), and supports --max-iteration.

For smoothing controls, run the analyzer module directly:

uv run python -m coolrl_lost_cities.games.classic.deep_cfr.analyze \
  --run runs/deep_cfr/<run-name> \
  --smoothing-window 5

Use --no-smoothing to force no moving average.

Current output files:

• analysis_01_loss.png
• analysis_02_match.png
• analysis_03_action.png
• analysis_04_gameflow.png
• analysis_05_open_quality.png
• analysis_06_expedition_outcomes.png
• analysis_07_calibration.png
• analysis_08_traversal.png
• analysis_09_selectivity.png
• analysis_final_eval_summary.png

Runtime Artifacts

Each training run writes:

• metrics.jsonl: structured metrics, one completed iteration per line.
• train.log: human-readable timestamped logs.
• runtime_progress.json: latest progress snapshot.
• latest.pt: latest checkpoint.
• iteration_*.pt: archive checkpoints when enabled.
• config.json: resolved config for the run.

If a run is stopped mid-iteration, the in-progress iteration may not appear in metrics.jsonl. Analyze the latest completed metric row.

Notes For Future Agents

• Prefer rg/rg --files for search.
• Use apply_patch for manual edits.
• Do not commit generated run artifacts from runs/.
• Cython-generated .c files are gitignored; edit .pyx/.pxd sources.
• Before committing, run uv run ruff check . and at least the relevant pytest subset. For Deep CFR changes, run uv run pytest -q tests/games/classic/test_deep_cfr_trainer.py.