- AGENTS.md: add Weights & Biases section covering install (extra), online/offline modes, per-run wandb/ layout, sync command, and the source-of-truth note (metrics.jsonl, not W&B). - CLAUDE.md: replace soft "before making changes" wording with a mandatory session-start instruction to read AGENTS.md in full. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
319 lines
8.7 KiB
Markdown
319 lines
8.7 KiB
Markdown
# AGENTS.md
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This repository is managed with `uv`. Use `uv run ...` for commands so the
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project environment and Cython extensions are built/loaded consistently.
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## Project Layout
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- `src/coolrl_lost_cities/games/classic/game.pyx`: Cython Lost Cities engine.
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- `src/coolrl_lost_cities/games/classic/deep_cfr/`: Deep CFR training,
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traversal, evaluation, analysis, and CLI code.
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- `configs/deep_cfr/`: Deep CFR YAML configs (kebab-case filenames).
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- `runs/`: generated training runs. Gitignored, may be a symlink to larger
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storage. Layout:
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- `runs/archive/`: past runs. **Do not modify or delete.**
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- `runs/tmp/`: smoke, tests, throwaway. Free to `rm -rf` anytime.
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- `runs/<YYYY-MM-DD_HHMMSS>_<kebab-name>/`: real experiments (flat).
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Promote a `runs/<...>` directory to `runs/archive/` with a manual `mv`
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once analysis is complete.
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- `docs/`: profiling notes, migration notes, and experiment documentation.
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## Core Commands
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Run lint:
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```bash
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uv run ruff check .
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```
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Run all tests:
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```bash
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uv run pytest -q
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```
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Run focused Deep CFR tests:
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```bash
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uv run pytest -q tests/games/classic/test_deep_cfr_trainer.py
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```
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Run the CLI through the console script:
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```bash
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uv run lost-cities-deep-cfr --help
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```
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Equivalent module form:
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```bash
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uv run python -m coolrl_lost_cities.games.classic.deep_cfr.cli --help
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```
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## Deep CFR Training
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The CLI auto-derives the run directory from `run.experiment_name` plus a
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timestamp. By default runs land under `runs/tmp/`; pass `--keep` for a real
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experiment that should live under `runs/`.
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Smoke / throwaway run (lands in `runs/tmp/`):
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```bash
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uv run lost-cities-deep-cfr train --config configs/deep_cfr/smoke.yaml
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# → runs/tmp/<YYYY-MM-DD_HHMMSS>_smoke/
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```
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Real experiment (lands in `runs/`):
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```bash
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uv run lost-cities-deep-cfr train \
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--config configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability.yaml \
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--keep
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# → runs/<YYYY-MM-DD_HHMMSS>_lost-cities-deep-cfr-selfplay-full-depth-slot-playability/
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```
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Variant of the same config (override slug):
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```bash
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uv run lost-cities-deep-cfr train \
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--config configs/deep_cfr/deep-cfr-color-shared-attention-512x3.yaml \
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--keep \
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--set run.experiment_name=color-attn-v2
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# → runs/<YYYY-MM-DD_HHMMSS>_color-attn-v2/
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```
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Short fixed-iteration run:
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```bash
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uv run lost-cities-deep-cfr train \
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--config configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability.yaml \
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--set run.max_iterations=100 \
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--set run.max_minutes=null \
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--set checkpoint.save_every=0
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```
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Resume (path required, no shortcut):
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```bash
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uv run lost-cities-deep-cfr train \
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--config configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability.yaml \
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--resume runs/<YYYY-MM-DD_HHMMSS>_<slug>/latest.pt
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```
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When `--resume` is given, the trainer reuses the resumed checkpoint's parent
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directory; no new timestamped folder is created.
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Useful train controls:
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- `--keep`: real experiment, write under `runs/` (default is `runs/tmp/`).
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- `--resume PATH`: resume from a specific checkpoint. `PATH` is required.
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- `--set PATH=VALUE`: override config fields. Repeatable, parses values as
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YAML (e.g. `--set traversal.num_workers=4`, `--set run.max_minutes=null`).
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Common `--set` overrides:
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- `--set run.device=cuda`: set the trainer device.
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- `--set run.experiment_name=foo-v2`: change the slug used in the run dir
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name (kebab-case).
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- `--set checkpoint.exact_resume=true`: require checkpoint config compatibility.
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- `--set checkpoint.save_latest=false --set checkpoint.save_every=0`:
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disable checkpoint writes.
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- `--set checkpoint.save_every=0`: keep only `latest.pt` (no archives).
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- `--set checkpoint.save_every=N`: archive every N iterations.
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## Naming Conventions
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- **Directory names, run dirs, config filenames, `experiment_name` values**:
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kebab-case (`deep-cfr-color-shared-512x3.yaml`,
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`runs/2026-05-08_103045_color-attn-v2/`).
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- **YAML keys, Python identifiers, config field names**: snake_case
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(unchanged: `hidden_size`, `traversals_per_player`, `experiment_name`).
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- The CLI converts `run.experiment_name` to a kebab slug when building the
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run directory, so values may contain spaces or mixed case.
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## Long Runs
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Run long jobs in a real `tmux` session so the user can attach and stop them.
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Do not rely on Codex command sessions for long user-observable training runs.
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Start a long unbounded run:
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```bash
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tmux new-session -s coolrl-deepcfr-unbounded \
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-c /home/coolguy/dev/coolrl-lost-cities \
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'uv run lost-cities-deep-cfr train \
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--config configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability-unbounded.yaml \
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--keep'
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```
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Attach later:
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```bash
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tmux attach -t coolrl-deepcfr-unbounded
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```
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Detach without stopping:
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```text
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Ctrl+B, D
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```
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Stop training:
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```text
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Ctrl+C
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```
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Follow logs from another terminal:
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```bash
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tail -f runs/<YYYY-MM-DD_HHMMSS>_<slug>/train.log
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```
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The unbounded config intentionally has:
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```yaml
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run:
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max_iterations: null
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max_minutes: null
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checkpoint:
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save_every: 100
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```
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`latest.pt` is updated continuously; archive checkpoints are written every 100
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iterations. If disk is tight, set `--set checkpoint.save_every=0` (keep only
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`latest.pt`) or increase `save_every`.
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## Evaluation And Analysis
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Evaluate a checkpoint:
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```bash
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uv run lost-cities-deep-cfr eval \
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--checkpoint runs/<run-dir>/latest.pt \
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--opponent random \
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--games 100 \
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--device cpu
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```
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Save evaluation game records:
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```bash
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uv run lost-cities-deep-cfr eval \
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--checkpoint runs/<run-dir>/latest.pt \
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--opponent random \
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--games 100 \
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--device cpu \
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--save-games runs/<run-dir>/eval_random_games.json
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```
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Generate analysis plots from `metrics.jsonl`:
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```bash
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uv run lost-cities-deep-cfr analyze \
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--run runs/<run-dir>
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```
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Write plots to a separate directory:
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```bash
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uv run lost-cities-deep-cfr analyze \
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--run runs/<run-dir> \
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--output-dir runs/<run-dir>/analysis
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```
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The analyzer reads `metrics.jsonl` and writes PNG files grouped by diagnostic
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section. Opponents are compared within each plot using fixed colors. The
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`lost-cities-deep-cfr analyze` subcommand uses the analyzer default smoothing
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window, currently 1 iteration (no smoothing), and supports `--max-iteration`.
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For smoothing controls, run the analyzer module directly:
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```bash
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uv run python -m coolrl_lost_cities.games.classic.deep_cfr.analyze \
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--run runs/<run-dir> \
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--smoothing-window 5
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```
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Use `--no-smoothing` to force no moving average.
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Current output files:
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- `analysis_01_loss.png`
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- `analysis_02_match.png`
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- `analysis_03_action.png`
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- `analysis_04_gameflow.png`
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- `analysis_05_open_quality.png`
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- `analysis_06_expedition_outcomes.png`
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- `analysis_07_calibration.png`
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- `analysis_08_traversal.png`
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- `analysis_09_selectivity.png`
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- `analysis_final_eval_summary.png`
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## Runtime Artifacts
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Each training run writes:
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- `metrics.jsonl`: structured metrics, one completed iteration per line.
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- `train.log`: human-readable timestamped logs.
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- `runtime_progress.json`: latest progress snapshot.
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- `latest.pt`: latest checkpoint.
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- `iteration_*.pt`: archive checkpoints when enabled.
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- `config.json`: resolved config for the run.
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If a run is stopped mid-iteration, the in-progress iteration may not appear in
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`metrics.jsonl`. Analyze the latest completed metric row.
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## Weights & Biases (Optional)
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Metrics can be mirrored to W&B. `wandb` is an optional extra; default
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installs and runs do not require it.
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Install:
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```bash
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uv sync --extra wandb
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```
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Run with W&B:
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```bash
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# Offline: no login, writes to <run_dir>/wandb/offline-run-*/
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uv run lost-cities-deep-cfr train --config <...> --wandb --wandb-mode offline
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# Online: requires `uv run wandb login` once, then real-time upload
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uv run lost-cities-deep-cfr train --config <...> --wandb
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```
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W&B data is stored **per run** at `<run_dir>/wandb/`, not at a global
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`runs/wandb/`. Each training run gets its own subfolder, so moving or
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deleting a run directory carries its W&B data along with it.
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Sync offline runs to wandb.ai later:
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```bash
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wandb sync runs/<run-dir>/wandb/offline-run-*
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```
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Flags:
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- `--wandb`: enable W&B mirroring.
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- `--wandb-project <name>`: defaults to `coolrl-lost-cities`.
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- `--wandb-name <name>`: W&B run name; defaults to `run.experiment_name`.
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- `--wandb-mode {online,offline,disabled}`: default `online`.
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- `--wandb-tag <tag>`: tag the run; repeatable.
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W&B is purely additive — `metrics.jsonl` remains the source of truth, and
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`analyze` reads `metrics.jsonl`, not W&B. Disabling W&B never breaks
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training, resume, or analysis.
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## Notes For Future Agents
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- Prefer `rg`/`rg --files` for search.
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- Use `apply_patch` for manual edits.
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- Do not commit generated run artifacts from `runs/`.
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- Cython-generated `.c` files are gitignored; edit `.pyx`/`.pxd` sources.
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- Before committing, run `uv run ruff check .` and at least the relevant pytest
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subset. For Deep CFR changes, run
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`uv run pytest -q tests/games/classic/test_deep_cfr_trainer.py`.
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