From cdc0d3961b8a5821a38412de28d0e11685166246 Mon Sep 17 00:00:00 2001
From: TohruSu <238259854+TohruSu@users.noreply.github.com>
Date: Thu, 15 Oct 2026 12:08:59 +0000
Subject: [PATCH] Add reproducible scheduling benchmark and experimental
 comparison

---
 .github/workflows/checks.yml                  |    2 +-
 .gitignore                                    |    3 +
 README.md                                     |   20 +-
 benchmarks/default/action_distribution.csv    |   37 +
 .../default/figures/deadline_miss_rate.png    |  Bin 0 -> 95900 bytes
 .../default/figures/scheduling_cost.png       |  Bin 0 -> 93848 bytes
 .../default/models/qlearning_seed_2042.json   | 1153 +++++++++++++++++
 .../default/models/qlearning_seed_4042.json   | 1125 ++++++++++++++++
 .../default/models/qlearning_seed_42.json     | 1125 ++++++++++++++++
 benchmarks/default/per_episode.csv            |  721 +++++++++++
 benchmarks/default/protocol.json              |   67 +
 benchmarks/default/summary.csv                |   25 +
 docs/experiments.md                           |  102 ++
 pyproject.toml                                |    1 +
 src/adaptivequeue/benchmark.py                |  280 ++++
 src/adaptivequeue/cli.py                      |   13 +
 tests/test_benchmark.py                       |  167 +++
 tests/test_cli.py                             |   22 +
 18 files changed, 4858 insertions(+), 5 deletions(-)
 create mode 100644 benchmarks/default/action_distribution.csv
 create mode 100644 benchmarks/default/figures/deadline_miss_rate.png
 create mode 100644 benchmarks/default/figures/scheduling_cost.png
 create mode 100644 benchmarks/default/models/qlearning_seed_2042.json
 create mode 100644 benchmarks/default/models/qlearning_seed_4042.json
 create mode 100644 benchmarks/default/models/qlearning_seed_42.json
 create mode 100644 benchmarks/default/per_episode.csv
 create mode 100644 benchmarks/default/protocol.json
 create mode 100644 benchmarks/default/summary.csv
 create mode 100644 docs/experiments.md
 create mode 100644 src/adaptivequeue/benchmark.py
 create mode 100644 tests/test_benchmark.py

diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml
index abdee7d..1c7ae3b 100644
--- a/.github/workflows/checks.yml
+++ b/.github/workflows/checks.yml
@@ -13,7 +13,7 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - run: python -m pip install -e '.[dev]'
+      - run: python -m pip install -e '.[dev,benchmark]'
       - run: python -m pytest
       - run: ruff check .
       - run: ruff format --check .
diff --git a/.gitignore b/.gitignore
index 6f596e1..51f5ed7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -218,3 +218,6 @@ __marimo__/
 
 # Streamlit
 .streamlit/secrets.toml
+
+# Default local benchmark output; reference results live in benchmarks/default.
+benchmark-results/
diff --git a/README.md b/README.md
index 2c0f095..6da910c 100644
--- a/README.md
+++ b/README.md
@@ -4,12 +4,13 @@ A lightweight Python project for experimenting with adaptive background job
 scheduling. Includes a discrete-time, single-worker simulator, FIFO, SPT, EDF,
 Priority, and seeded Random baselines, and a tabular Q-Learning meta-scheduler.
 
-Requires Python 3.10 or newer. There are no runtime dependencies.
+Requires Python 3.10 or newer. Simulation and learning have no runtime
+dependencies; benchmark figures use the optional Matplotlib extra.
 
 ```sh
 python -m venv .venv
 source .venv/bin/activate
-python -m pip install -e '.[dev]'
+python -m pip install -e '.[dev,benchmark]'
 ```
 
 Run an episode with 60 jobs:
@@ -35,6 +36,17 @@ Evaluation defaults to seeds following the saved training range, with exploratio
 disabled. Use `--seed` to choose a different disjoint range. Both commands accept
 the workload options below; `train --help` lists learning and exploration settings.
 
+Run the fixed paired benchmark (three independently trained models and all five
+baselines, across three workloads and 30 evaluation seeds):
+
+```sh
+adaptivequeue benchmark --output-dir benchmark-results
+```
+
+Models are cached in the output directory; `--retrain` regenerates them.
+See [experimental results and reproduction details](docs/experiments.md) for
+the observed comparisons and CSV definitions.
+
 ```sh
 python -m adaptivequeue --workload bursty --policy spt --seed 7 \
   --jobs 60 --arrival-rate 0.3 --deadline-factor 3
@@ -46,5 +58,5 @@ mypy
 
 See [simulation rules and parameters](docs/simulation.md) or run
 `adaptivequeue --help`. The Python API also exposes per-job execution records.
-The current scope is simulation, classical baselines, and CPU-only tabular
-Q-Learning; no additional runtime dependencies are needed.
+The current scope is simulation, classical baselines, CPU-only tabular
+Q-Learning, and reproducible benchmarking.
diff --git a/benchmarks/default/action_distribution.csv b/benchmarks/default/action_distribution.csv
new file mode 100644
index 0000000..f7817c0
--- /dev/null
+++ b/benchmarks/default/action_distribution.csv
@@ -0,0 +1,37 @@
+workload,training_seed,action,policy,selections,fraction,contended_selections,contended_fraction
+stable,42,0,fifo,191,0.10611111111111111,73,0.05615384615384615
+stable,42,1,spt,677,0.3761111111111111,590,0.45384615384615384
+stable,42,2,edf,263,0.1461111111111111,222,0.17076923076923076
+stable,42,3,priority,669,0.37166666666666665,415,0.3192307692307692
+stable,2042,0,fifo,202,0.11222222222222222,141,0.10854503464203233
+stable,2042,1,spt,377,0.20944444444444443,358,0.27559661277906083
+stable,2042,2,edf,818,0.45444444444444443,481,0.37028483448806776
+stable,2042,3,priority,403,0.2238888888888889,319,0.2455735180908391
+stable,4042,0,fifo,197,0.10944444444444444,184,0.1399239543726236
+stable,4042,1,spt,545,0.30277777777777776,522,0.39695817490494295
+stable,4042,2,edf,533,0.2961111111111111,266,0.20228136882129277
+stable,4042,3,priority,525,0.2916666666666667,343,0.26083650190114066
+bursty,42,0,fifo,36,0.02,30,0.018115942028985508
+bursty,42,1,spt,969,0.5383333333333333,949,0.5730676328502415
+bursty,42,2,edf,316,0.17555555555555555,231,0.13949275362318841
+bursty,42,3,priority,479,0.26611111111111113,446,0.2693236714975845
+bursty,2042,0,fifo,329,0.1827777777777778,256,0.15440289505428226
+bursty,2042,1,spt,741,0.4116666666666667,730,0.44028950542822676
+bursty,2042,2,edf,428,0.23777777777777778,395,0.2382388419782871
+bursty,2042,3,priority,302,0.16777777777777778,277,0.16706875753920386
+bursty,4042,0,fifo,148,0.08222222222222222,142,0.08559373116335142
+bursty,4042,1,spt,826,0.4588888888888889,768,0.4629294755877034
+bursty,4042,2,edf,338,0.18777777777777777,289,0.17420132610006028
+bursty,4042,3,priority,488,0.27111111111111114,460,0.27727546714888485
+mixed,42,0,fifo,190,0.10555555555555556,130,0.09537784299339692
+mixed,42,1,spt,642,0.3566666666666667,549,0.4027879677182685
+mixed,42,2,edf,209,0.11611111111111111,164,0.12032281731474688
+mixed,42,3,priority,759,0.4216666666666667,520,0.3815113719735877
+mixed,2042,0,fifo,261,0.145,155,0.1151560178306092
+mixed,2042,1,spt,517,0.2872222222222222,478,0.3551263001485884
+mixed,2042,2,edf,640,0.35555555555555557,401,0.29791976225854383
+mixed,2042,3,priority,382,0.21222222222222223,312,0.23179791976225855
+mixed,4042,0,fifo,240,0.13333333333333333,237,0.17400881057268722
+mixed,4042,1,spt,452,0.2511111111111111,441,0.3237885462555066
+mixed,4042,2,edf,683,0.3794444444444444,382,0.28046989720998533
+mixed,4042,3,priority,425,0.2361111111111111,302,0.22173274596182085
diff --git a/benchmarks/default/figures/deadline_miss_rate.png b/benchmarks/default/figures/deadline_miss_rate.png
new file mode 100644
index 0000000000000000000000000000000000000000..67bc5928b5ad1cfabbbdcccf7630a64db97af44c
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diff --git a/benchmarks/default/figures/scheduling_cost.png b/benchmarks/default/figures/scheduling_cost.png
new file mode 100644
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diff --git a/benchmarks/default/models/qlearning_seed_2042.json b/benchmarks/default/models/qlearning_seed_2042.json
new file mode 100644
index 0000000..086b299
--- /dev/null
+++ b/benchmarks/default/models/qlearning_seed_2042.json
@@ -0,0 +1,1153 @@
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diff --git a/benchmarks/default/models/qlearning_seed_4042.json b/benchmarks/default/models/qlearning_seed_4042.json
new file mode 100644
index 0000000..18db662
--- /dev/null
+++ b/benchmarks/default/models/qlearning_seed_4042.json
@@ -0,0 +1,1125 @@
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diff --git a/benchmarks/default/models/qlearning_seed_42.json b/benchmarks/default/models/qlearning_seed_42.json
new file mode 100644
index 0000000..3ebaf64
--- /dev/null
+++ b/benchmarks/default/models/qlearning_seed_42.json
@@ -0,0 +1,1125 @@
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diff --git a/benchmarks/default/per_episode.csv b/benchmarks/default/per_episode.csv
new file mode 100644
index 0000000..1966d08
--- /dev/null
+++ b/benchmarks/default/per_episode.csv
@@ -0,0 +1,721 @@
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diff --git a/benchmarks/default/protocol.json b/benchmarks/default/protocol.json
new file mode 100644
index 0000000..d94112f
--- /dev/null
+++ b/benchmarks/default/protocol.json
@@ -0,0 +1,67 @@
+{
+  "training_workload": "mixed",
+  "training_episodes": 1000,
+  "training_seeds": [
+    42,
+    2042,
+    4042
+  ],
+  "learning_config": {
+    "learning_rate": 0.1,
+    "gamma": 1.0,
+    "epsilon_start": 1.0,
+    "epsilon_end": 0.05,
+    "epsilon_decay": 0.995
+  },
+  "workload_config": {
+    "num_jobs": 60,
+    "arrival_rate": 0.28,
+    "deadline_factor": 4.0,
+    "duration_range": [
+      1,
+      6
+    ],
+    "burst_size": 6,
+    "burst_window": 3
+  },
+  "evaluation_workloads": [
+    "stable",
+    "bursty",
+    "mixed"
+  ],
+  "evaluation_seeds": [
+    20000,
+    20001,
+    20002,
+    20003,
+    20004,
+    20005,
+    20006,
+    20007,
+    20008,
+    20009,
+    20010,
+    20011,
+    20012,
+    20013,
+    20014,
+    20015,
+    20016,
+    20017,
+    20018,
+    20019,
+    20020,
+    20021,
+    20022,
+    20023,
+    20024,
+    20025,
+    20026,
+    20027,
+    20028,
+    20029
+  ],
+  "random_policy_seed": "evaluation_seed",
+  "aggregation": "mean of episode metrics; sample standard deviation of episode cost",
+  "python_version": "3.12.14"
+}
diff --git a/benchmarks/default/summary.csv b/benchmarks/default/summary.csv
new file mode 100644
index 0000000..d8a020b
--- /dev/null
+++ b/benchmarks/default/summary.csv
@@ -0,0 +1,25 @@
+workload,policy,training_seed,episodes,mean_primary_cost,mean_deadline_miss_rate,mean_average_waiting_time,mean_p95_waiting_time,mean_average_flow_time,std_primary_cost,mean_cost_delta_vs_fifo,cost_win_rate_vs_fifo,mean_cost_delta_vs_spt,cost_win_rate_vs_spt,mean_cost_delta_vs_edf,cost_win_rate_vs_edf,mean_cost_delta_vs_priority,cost_win_rate_vs_priority,mean_cost_delta_vs_random,cost_win_rate_vs_random
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+mixed,priority,,30,9.212111111111112,0.30333333333333334,11.855555555555556,45.179999999999986,15.388333333333332,6.942778766367459,-10.148333333333333,1.0,-3.439166666666666,0.8,-0.11566666666666683,0.26666666666666666,0.0,0.0,-13.177833333333334,1.0
+mixed,random,,30,22.389944444444446,0.375,12.008888888888889,39.396666666666654,15.541666666666666,15.845725576890123,3.0295,0.16666666666666666,9.738666666666667,0.0,13.062166666666666,0.0,13.177833333333334,0.0,0.0,0.0
+mixed,qlearning,42,30,9.28422222222222,0.24222222222222223,9.398333333333333,35.10499999999998,12.931111111111111,8.30007407451289,-10.076222222222222,1.0,-3.367055555555555,0.9333333333333333,-0.043555555555555785,0.3333333333333333,0.07211111111111106,0.5333333333333333,-13.105722222222223,1.0
+mixed,qlearning,2042,30,10.402444444444445,0.3194444444444445,9.866666666666667,30.40833333333332,13.399444444444445,9.442392371835844,-8.958,1.0,-2.2488333333333324,0.8333333333333334,1.0746666666666667,0.2,1.1903333333333337,0.5666666666666667,-11.9875,1.0
+mixed,qlearning,4042,30,9.899555555555557,0.28944444444444445,9.843333333333334,36.14833333333332,13.376111111111111,7.750913330601302,-9.460888888888888,1.0,-2.7517222222222215,0.8,0.5717777777777776,0.26666666666666666,0.6874444444444444,0.43333333333333335,-12.49038888888889,1.0
diff --git a/docs/experiments.md b/docs/experiments.md
new file mode 100644
index 0000000..3a5bf48
--- /dev/null
+++ b/docs/experiments.md
@@ -0,0 +1,102 @@
+# Scheduling experiments
+
+## Setup and reproduction
+
+Three independent Q-Learning models train on `mixed` for 1,000 episodes each,
+starting at seeds 42, 2042, and 4042 (workload seed ranges 42–1041, 2042–3041,
+and 4042–5041). Learning defaults are unchanged: alpha=0.1, gamma=1,
+epsilon=max(0.05, 0.995**episode). Each model is evaluated greedily with no
+exploration, learning, or Q-table insertion.
+
+Evaluation uses `stable`, `bursty`, and `mixed`, each at seeds 20000–20029.
+The generators produce 60 jobs with duration 1–6, weights 1/2/4, arrival rate
+0.28, deadline factor 4, burst size 6, and burst window 3. Each workload/seed
+is generated once and the same immutable job specifications go to all eight
+policy instances: five baselines and three trained models. Random uses a separate
+local RNG seeded with that episode's evaluation seed.
+
+Before the final run, seeds 7 and 42 were checked on all three workloads. All five
+baselines produced distinct execution orders in each episode. On FIFO's queue
+snapshots, 54–59 decisions had multiple jobs waiting and the deterministic
+heuristics disagreed at 48–57 of them. The final protocol above was then kept fixed;
+there was no hyperparameter tuning or selection of a preferred trained model.
+
+```sh
+python -m pip install -e '.[benchmark]'
+adaptivequeue benchmark --output-dir benchmark-results --retrain
+```
+
+Omit `--retrain` to reuse the three saved models. Cached models must match the
+training seed, episode count, workload configuration, and learning settings.
+The committed [reference outputs](../benchmarks/default) were generated with
+Python 3.12.14 and Matplotlib 3.11.2.
+
+## Output definitions
+
+- `per_episode.csv`: 720 rows, with all five scheduling metrics for each
+  workload/evaluation-seed/policy/training-seed combination. Baselines have a
+  blank training seed and are evaluated once, not repeated for each RL run.
+- `summary.csv`: 24 rows, one per workload/policy/training run. Metrics are
+  arithmetic means of the 30 **episode metrics**. In particular, mean P95 waiting
+  time averages episode percentiles; it is not a percentile over pooled jobs.
+  `std_primary_cost` is the sample standard deviation over evaluation episodes,
+  not training variability or a confidence interval.
+- `mean_cost_delta_vs_*` is mean paired `J(policy) - J(baseline)` using matching
+  evaluation seeds; negative favors the policy. `cost_win_rate_vs_*` is the
+  fraction of those 30 paired episodes with strictly lower cost; ties are not wins.
+- `action_distribution.csv`: 36 rows, counting each underlying action separately
+  for each workload and trained model. `fraction` covers all 1,800 decisions;
+  `contended_fraction` covers only decisions with at least two jobs waiting.
+- `figures/scheduling_cost.png` and `figures/deadline_miss_rate.png`: episode
+  distributions, with median lines and mean triangles. Each panel uses its own
+  vertical scale. Models remain separate; 30 episodes per box.
+- `models/` and `protocol.json`: the three Q-tables and complete experiment settings.
+
+## Observed results
+
+Mean primary cost J over the same 30 evaluation episodes; lower is better:
+
+| Policy / training seed | Stable | Bursty | Mixed |
+| --- | ---: | ---: | ---: |
+| FIFO | 16.531 | 52.744 | 19.360 |
+| SPT | 10.415 | 31.018 | 12.651 |
+| EDF | 11.441 | 39.999 | 9.328 |
+| Priority | 9.852 | 30.867 | **9.212** |
+| Random | 18.647 | 56.171 | 22.390 |
+| Q-Learning / 42 | 9.270 | 28.804 | 9.284 |
+| Q-Learning / 2042 | 10.164 | 31.471 | 10.402 |
+| Q-Learning / 4042 | **9.210** | **27.130** | 9.900 |
+
+Priority has the lowest mean cost among the classical baselines in all three
+workloads. Models 42 and 4042 improve on it in stable and bursty workloads:
+their paired mean differences are -0.582/-0.642 on stable and -2.062/-3.736 on
+bursty. They win 23/22 of 30 stable episodes and 24/26 bursty episodes respectively.
+Model 2042 is worse than Priority in both means (+0.312 and +0.605), despite
+winning 19/30 paired episodes in each. Mean improvement and win frequency answer
+different questions when cost differences vary substantially between episodes.
+
+On `mixed`, the training workload, **all three models lose to Priority in mean
+cost**: +0.072, +1.190, and +0.687 respectively. Model 42 narrowly beats EDF
+in mean (-0.044), while models 2042 and 4042 lose to EDF (+1.075/+0.572).
+Every model beats FIFO and Random in mean on every workload, but those comparisons
+alone would overstate the value relative to stronger heuristics.
+
+SPT has the lowest mean deadline-miss rate in every workload: 14.94%, 35.00%, and
+20.72%. The best RL miss rates are 22.17%, 44.06%, and 24.22%. J penalizes weighted
+tardiness and flow time rather than the unweighted count of misses, so the cost
+ranking does not imply the same ranking on deadline-miss rate or waiting time.
+All secondary metrics are retained in the CSVs.
+
+Action mixes also differ across training runs. On bursty workloads, model 42
+selects FIFO/SPT/EDF/Priority for 2.0%/53.8%/17.6%/26.6% of all decisions;
+model 2042 uses 18.3%/41.2%/23.8%/16.8%, and model 4042 uses
+8.2%/45.9%/18.8%/27.1%. Choices at single-job queues cannot affect that dispatch;
+the CSV includes contended-only frequencies to distinguish these cases.
+
+These runs show useful cost reductions for two training seeds on stable and
+bursty loads, but no consistent advantage over a strong heuristic, even on mixed
+loads. Three training seeds and one load/deadline setting do not establish broad
+superiority. The models share the same 30 evaluation workloads per family, so their
+results should not be pooled as 90 independent evaluation episodes. The coarse
+state also maps different queues to the same observation; see the
+[state design](simulation.md#tabular-q-learning-meta-scheduler).
diff --git a/pyproject.toml b/pyproject.toml
index 299c35b..2499b44 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,6 +13,7 @@ dependencies = []
 
 [project.optional-dependencies]
 dev = ["pytest>=8", "ruff>=0.9", "mypy>=1.14"]
+benchmark = ["matplotlib>=3.8"]
 
 [project.scripts]
 adaptivequeue = "adaptivequeue.cli:main"
diff --git a/src/adaptivequeue/benchmark.py b/src/adaptivequeue/benchmark.py
new file mode 100644
index 0000000..49ee13a
--- /dev/null
+++ b/src/adaptivequeue/benchmark.py
@@ -0,0 +1,280 @@
+"""Paired scheduling experiments with separate Q-Learning training runs."""
+
+import csv
+import json
+import platform
+from dataclasses import asdict, dataclass, fields
+from pathlib import Path
+from statistics import fmean, stdev
+from typing import Sequence
+
+from adaptivequeue.metrics import Metrics
+from adaptivequeue.models import Job
+from adaptivequeue.policies import POLICY_NAMES, make_policy
+from adaptivequeue.qlearning import (
+    ACTION_NAMES,
+    ACTION_POLICIES,
+    LearningConfig,
+    QLearningAgent,
+    TrainingInfo,
+    encode_state,
+    train,
+)
+from adaptivequeue.simulation import simulate
+from adaptivequeue.workloads import WORKLOADS, WorkloadConfig
+
+TRAINING_SEEDS = (42, 2042, 4042)
+EVALUATION_SEEDS = tuple(range(20000, 20030))
+METRIC_NAMES = tuple(field.name for field in fields(Metrics))
+
+
+@dataclass(frozen=True)
+class EpisodeResult:
+    workload: str
+    evaluation_seed: int
+    policy: str
+    training_seed: int | None
+    metrics: Metrics
+
+
+def summarize(results: Sequence[EpisodeResult]) -> list[dict[str, object]]:
+    """Average episode metrics and compare paired costs, without pooling RL runs."""
+    groups: dict[tuple[str, str, int | None], list[EpisodeResult]] = {}
+    baselines = {
+        (row.workload, row.evaluation_seed, row.policy): row.metrics.primary_cost
+        for row in results
+        if row.training_seed is None
+    }
+    for row in results:
+        groups.setdefault((row.workload, row.policy, row.training_seed), []).append(row)
+    summary = []
+    for (workload, policy, training_seed), episodes in groups.items():
+        row_data: dict[str, object] = {
+            "workload": workload,
+            "policy": policy,
+            "training_seed": training_seed,
+            "episodes": len(episodes),
+        }
+        row_data.update(
+            {
+                f"mean_{name}": fmean(getattr(episode.metrics, name) for episode in episodes)
+                for name in METRIC_NAMES
+            }
+        )
+        row_data["std_primary_cost"] = (
+            stdev(episode.metrics.primary_cost for episode in episodes)
+            if len(episodes) > 1
+            else 0.0
+        )
+        for baseline in POLICY_NAMES:
+            differences = [
+                episode.metrics.primary_cost
+                - baselines[(workload, episode.evaluation_seed, baseline)]
+                for episode in episodes
+            ]
+            row_data[f"mean_cost_delta_vs_{baseline}"] = fmean(differences)
+            row_data[f"cost_win_rate_vs_{baseline}"] = fmean(value < 0 for value in differences)
+        summary.append(row_data)
+    return summary
+
+
+def _write_csv(path: Path, rows: Sequence[dict[str, object]]) -> None:
+    with path.open("w", newline="", encoding="utf-8") as stream:
+        writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def _load_or_train(
+    directory: Path, seeds: Sequence[int], episodes: int, *, retrain: bool
+) -> dict[int, QLearningAgent]:
+    directory.mkdir(parents=True, exist_ok=True)
+    agents = {}
+    for seed in seeds:
+        path = directory / f"qlearning_seed_{seed}.json"
+        if path.exists() and not retrain:
+            agent = QLearningAgent.load(path)
+            expected = TrainingInfo("mixed", WorkloadConfig(), episodes, seed)
+            if agent.training != expected or agent.config != LearningConfig() or agent.seed != seed:
+                raise ValueError(f"cached model configuration differs: {path}; use --retrain")
+        else:
+            agent = train(
+                workload="mixed",
+                episodes=episodes,
+                seed=seed,
+                workload_config=WorkloadConfig(),
+                learning_config=LearningConfig(),
+            )
+            agent.save(path)
+        agents[seed] = agent
+    return agents
+
+
+def _evaluate_agent(
+    agent: QLearningAgent, jobs: Sequence[Job]
+) -> tuple[Metrics, list[int], list[int]]:
+    counts = [0] * len(ACTION_NAMES)
+    contended = [0] * len(ACTION_NAMES)
+
+    def policy(waiting_jobs: Sequence[Job], current_time: int) -> Job:
+        action = agent.greedy_action(encode_state(waiting_jobs, current_time))
+        counts[action] += 1
+        contended[action] += len(waiting_jobs) > 1
+        return ACTION_POLICIES[action](waiting_jobs, current_time)
+
+    return simulate(jobs, policy).metrics, counts, contended
+
+
+def plot_results(results: Sequence[EpisodeResult], output_dir: Path) -> None:
+    try:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+    except ImportError as error:
+        raise ValueError(
+            "plotting requires Matplotlib; install adaptivequeue[benchmark]"
+        ) from error
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    workloads = list(dict.fromkeys(row.workload for row in results))
+    series = list(dict.fromkeys((row.policy, row.training_seed) for row in results))
+    labels = [name.upper() if seed is None else f"QL {seed}" for name, seed in series]
+    for metric, filename, label in (
+        ("primary_cost", "scheduling_cost.png", "Scheduling cost J (lower is better)"),
+        ("deadline_miss_rate", "deadline_miss_rate.png", "Deadline miss rate"),
+    ):
+        figure, axes = plt.subplots(
+            1, len(workloads), figsize=(5 * len(workloads), 4.8), squeeze=False
+        )
+        for axis, workload in zip(axes[0], workloads):
+            values = [
+                [
+                    getattr(row.metrics, metric)
+                    for row in results
+                    if row.workload == workload and (row.policy, row.training_seed) == key
+                ]
+                for key in series
+            ]
+            boxes = axis.boxplot(values, showmeans=True, patch_artist=True)
+            for index, box in enumerate(boxes["boxes"]):
+                box.set_facecolor(plt.get_cmap("tab10")(index))
+                box.set_alpha(0.55)
+            axis.set_xticks(range(1, len(series) + 1), labels, rotation=45, ha="right")
+            axis.set_title(workload)
+            axis.set_ylabel(label)
+            axis.set_ylim(bottom=0)
+            axis.grid(axis="y", alpha=0.25)
+        figure.suptitle("Episode distributions; line = median, triangle = mean")
+        figure.tight_layout()
+        figure.savefig(output_dir / filename, dpi=150)
+        plt.close(figure)
+
+
+def run_benchmark(
+    output_dir: str | Path,
+    *,
+    training_seeds: Sequence[int] = TRAINING_SEEDS,
+    training_episodes: int = 1000,
+    evaluation_seeds: Sequence[int] = EVALUATION_SEEDS,
+    workloads: Sequence[str] = ("stable", "bursty", "mixed"),
+    retrain: bool = False,
+    plots: bool = True,
+) -> list[EpisodeResult]:
+    """Run the fixed protocol; smaller seed sets are useful for focused tests."""
+    if training_episodes < 1 or not training_seeds or not evaluation_seeds or not workloads:
+        raise ValueError(
+            "benchmark needs positive training episodes and nonempty seed/workload sets"
+        )
+    for seeds in (training_seeds, evaluation_seeds):
+        if len(set(seeds)) != len(seeds) or min(seeds) < 0:
+            raise ValueError("benchmark seeds must be unique and nonnegative")
+    if any(name not in WORKLOADS for name in workloads) or len(set(workloads)) != len(workloads):
+        raise ValueError("benchmark workloads must be unique registered workload names")
+    if any(
+        start <= seed < start + training_episodes
+        for start in training_seeds
+        for seed in evaluation_seeds
+    ):
+        raise ValueError("evaluation seeds must not overlap training seeds")
+
+    directory = Path(output_dir)
+    agents = _load_or_train(
+        directory / "models", training_seeds, training_episodes, retrain=retrain
+    )
+    results = []
+    counts = {
+        (workload, seed): [0] * len(ACTION_NAMES)
+        for workload in workloads
+        for seed in training_seeds
+    }
+    contended = {
+        (workload, seed): [0] * len(ACTION_NAMES)
+        for workload in workloads
+        for seed in training_seeds
+    }
+    for workload in workloads:
+        for evaluation_seed in evaluation_seeds:
+            jobs = WORKLOADS[workload](evaluation_seed, WorkloadConfig())
+            for name in POLICY_NAMES:
+                metrics = simulate(jobs, make_policy(name, seed=evaluation_seed)).metrics
+                results.append(EpisodeResult(workload, evaluation_seed, name, None, metrics))
+            for training_seed, agent in agents.items():
+                metrics, selections, choices = _evaluate_agent(agent, jobs)
+                results.append(
+                    EpisodeResult(workload, evaluation_seed, "qlearning", training_seed, metrics)
+                )
+                for action in range(len(ACTION_NAMES)):
+                    counts[(workload, training_seed)][action] += selections[action]
+                    contended[(workload, training_seed)][action] += choices[action]
+
+    episode_rows = [
+        {
+            "workload": row.workload,
+            "evaluation_seed": row.evaluation_seed,
+            "policy": row.policy,
+            "training_seed": row.training_seed,
+            **asdict(row.metrics),
+        }
+        for row in results
+    ]
+    action_rows: list[dict[str, object]] = []
+    for (workload, seed), selections in counts.items():
+        choices = contended[(workload, seed)]
+        total, total_contended = sum(selections), sum(choices)
+        for action, name in enumerate(ACTION_NAMES):
+            action_rows.append(
+                {
+                    "workload": workload,
+                    "training_seed": seed,
+                    "action": action,
+                    "policy": name,
+                    "selections": selections[action],
+                    "fraction": selections[action] / total if total else 0.0,
+                    "contended_selections": choices[action],
+                    "contended_fraction": choices[action] / total_contended
+                    if total_contended
+                    else 0.0,
+                }
+            )
+    _write_csv(directory / "per_episode.csv", episode_rows)
+    _write_csv(directory / "summary.csv", summarize(results))
+    _write_csv(directory / "action_distribution.csv", action_rows)
+    protocol = {
+        "training_workload": "mixed",
+        "training_episodes": training_episodes,
+        "training_seeds": list(training_seeds),
+        "learning_config": asdict(LearningConfig()),
+        "workload_config": asdict(WorkloadConfig()),
+        "evaluation_workloads": list(workloads),
+        "evaluation_seeds": list(evaluation_seeds),
+        "random_policy_seed": "evaluation_seed",
+        "aggregation": "mean of episode metrics; sample standard deviation of episode cost",
+        "python_version": platform.python_version(),
+    }
+    (directory / "protocol.json").write_text(
+        json.dumps(protocol, indent=2) + "\n", encoding="utf-8"
+    )
+    if plots:
+        plot_results(results, directory / "figures")
+    return results
diff --git a/src/adaptivequeue/cli.py b/src/adaptivequeue/cli.py
index c4d050c..ab50d75 100644
--- a/src/adaptivequeue/cli.py
+++ b/src/adaptivequeue/cli.py
@@ -54,9 +54,22 @@ def main() -> None:
     _add_workload_arguments(evaluation, workload="mixed", seed=None)
     evaluation.add_argument("--episodes", type=int, default=20)
     evaluation.add_argument("--model", type=Path, required=True)
+
+    benchmark = commands.add_parser("benchmark", help="Run the reproducible paired benchmark")
+    benchmark.add_argument("--output-dir", type=Path, default=Path("benchmark-results"))
+    benchmark.add_argument("--retrain", action="store_true", help="Replace cached benchmark models")
     args = parser.parse_args()
 
     try:
+        if args.command == "benchmark":
+            from adaptivequeue.benchmark import run_benchmark
+
+            results = run_benchmark(args.output_dir, retrain=args.retrain)
+            print(
+                f"Saved {len(results)} per-episode results, "
+                f"summaries, and figures to {args.output_dir}"
+            )
+            return
         config = WorkloadConfig(
             num_jobs=args.jobs,
             arrival_rate=args.arrival_rate,
diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py
new file mode 100644
index 0000000..7b22a89
--- /dev/null
+++ b/tests/test_benchmark.py
@@ -0,0 +1,167 @@
+import copy
+import csv
+import json
+import random
+from pathlib import Path
+from typing import Iterable
+
+import pytest
+
+from adaptivequeue import Job, Metrics, SimulationResult, WorkloadConfig, benchmark, simulate
+from adaptivequeue.benchmark import EpisodeResult, run_benchmark, summarize
+from adaptivequeue.policies import POLICY_NAMES, Policy
+from adaptivequeue.qlearning import QLearningAgent, train
+from adaptivequeue.workloads import WORKLOADS
+
+
+def test_fresh_and_cached_benchmarks_are_reproducible(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    settings = {
+        "training_seeds": (42, 2042),
+        "training_episodes": 2,
+        "evaluation_seeds": (20000, 20001),
+        "workloads": ("stable", "mixed"),
+        "plots": False,
+    }
+    global_state = random.getstate()
+    first, second = tmp_path / "first", tmp_path / "second"
+    results = run_benchmark(first, **settings)
+    assert results == run_benchmark(second, **settings)
+    assert len(results) == 2 * 2 * (5 + 2)
+    for name in ("per_episode.csv", "summary.csv", "action_distribution.csv", "protocol.json"):
+        assert (first / name).read_bytes() == (second / name).read_bytes()
+
+    def fail(**kwargs: object) -> QLearningAgent:
+        pytest.fail("a cached benchmark must not retrain")
+
+    monkeypatch.setattr(benchmark, "train", fail)
+    assert run_benchmark(first, **settings) == results
+    assert random.getstate() == global_state
+    with (first / "action_distribution.csv").open() as stream:
+        actions = list(csv.DictReader(stream))
+    assert len(actions) == 2 * 2 * 4
+    for workload in settings["workloads"]:
+        for seed in settings["training_seeds"]:
+            rows = [
+                r for r in actions if r["workload"] == workload and int(r["training_seed"]) == seed
+            ]
+            assert sum(int(r["selections"]) for r in rows) == 2 * 60
+            assert sum(float(r["fraction"]) for r in rows) == pytest.approx(1.0)
+            assert sum(float(r["contended_fraction"]) for r in rows) == pytest.approx(1.0)
+            assert all(int(r["contended_selections"]) <= int(r["selections"]) for r in rows)
+
+
+def test_every_policy_receives_the_same_generated_jobs(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    generated: dict[int, list[Job]] = {}
+    observed: list[tuple[Job, ...]] = []
+
+    def generate(seed: int, config: WorkloadConfig) -> list[Job]:
+        assert seed not in generated
+        generated[seed] = WORKLOADS["mixed"](seed, config)
+        return generated[seed]
+
+    def observe(jobs: Iterable[Job], policy: Policy) -> SimulationResult:
+        assert jobs is generated[20000]
+        observed.append(tuple(jobs))
+        return simulate(jobs, policy)
+
+    monkeypatch.setattr(benchmark, "WORKLOADS", {"mixed": generate})
+    monkeypatch.setattr(benchmark, "simulate", observe)
+    run_benchmark(
+        tmp_path,
+        training_seeds=(42,),
+        training_episodes=2,
+        evaluation_seeds=(20000,),
+        workloads=("mixed",),
+        plots=False,
+    )
+    assert len(generated) == 1
+    assert len(observed) == 6
+    assert all(jobs == observed[0] for jobs in observed)
+    assert tuple(generated[20000]) == observed[0]
+
+
+def test_aggregation_keeps_runs_separate_and_pairs_evaluation_seeds() -> None:
+    results = []
+    for seed, cost in ((1, 10.0), (2, 20.0)):
+        for policy in POLICY_NAMES:
+            results.append(EpisodeResult("mixed", seed, policy, None, Metrics(cost, 0.2, 2, 4, 6)))
+        for training_seed, rl_cost in ((42, 8.0 if seed == 1 else 22.0), (2042, cost + 4)):
+            metrics = Metrics(rl_cost, 0.1 * seed, 2 * seed, 10 * seed, 5 * seed)
+            results.append(EpisodeResult("mixed", seed, "qlearning", training_seed, metrics))
+    summary = summarize(results)
+    assert len(summary) == 7
+    runs = {row["training_seed"]: row for row in summary if row["policy"] == "qlearning"}
+    assert runs[42]["episodes"] == 2
+    assert runs[42]["mean_primary_cost"] == 15
+    assert runs[42]["std_primary_cost"] == pytest.approx(98**0.5)
+    assert runs[42]["mean_cost_delta_vs_fifo"] == 0
+    assert runs[42]["cost_win_rate_vs_fifo"] == 0.5
+    assert runs[42]["mean_deadline_miss_rate"] == pytest.approx(0.15)
+    assert runs[42]["mean_p95_waiting_time"] == 15
+    assert runs[2042]["mean_primary_cost"] == 19
+    assert runs[2042]["mean_cost_delta_vs_fifo"] == 4
+    assert runs[2042]["cost_win_rate_vs_fifo"] == 0
+
+
+def test_benchmark_evaluation_is_frozen_and_counts_greedy_actions(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    agent = train(episodes=2, seed=42)
+    table_before = copy.deepcopy(agent.q_table)
+    rng_before = agent._rng.getstate()
+    expected = simulate(WORKLOADS["mixed"](20000, WorkloadConfig()), agent.policy).metrics
+
+    def fail(*args: object, **kwargs: object) -> None:
+        pytest.fail("benchmark evaluation must not explore or learn")
+
+    monkeypatch.setattr(agent, "choose_action", fail)
+    monkeypatch.setattr(agent, "update", fail)
+    monkeypatch.setattr(benchmark, "_load_or_train", lambda *args, **kwargs: {42: agent})
+    rows = run_benchmark(
+        tmp_path,
+        training_seeds=(42,),
+        training_episodes=2,
+        evaluation_seeds=(20000,),
+        workloads=("mixed",),
+        plots=False,
+    )
+    assert rows[-1].metrics == expected
+    assert agent.q_table == table_before
+    assert agent._rng.getstate() == rng_before
+    with (tmp_path / "action_distribution.csv").open() as stream:
+        actions = list(csv.DictReader(stream))
+    assert sum(int(row["selections"]) for row in actions) == 60
+
+
+def test_cached_model_configuration_must_match(tmp_path: Path) -> None:
+    settings = {
+        "training_seeds": (42,),
+        "training_episodes": 1,
+        "evaluation_seeds": (20000,),
+        "workloads": ("mixed",),
+        "plots": False,
+    }
+    run_benchmark(tmp_path, **settings)
+    path = tmp_path / "models" / "qlearning_seed_42.json"
+    data = json.loads(path.read_text())
+    data["training"]["episodes"] = 999
+    path.write_text(json.dumps(data))
+    with pytest.raises(ValueError, match="cached model configuration"):
+        run_benchmark(tmp_path, **settings)
+
+
+def test_plot_files_are_generated(tmp_path: Path) -> None:
+    pytest.importorskip("matplotlib")
+    run_benchmark(
+        tmp_path,
+        training_seeds=(42,),
+        training_episodes=1,
+        evaluation_seeds=(20000, 20001),
+        workloads=("mixed",),
+    )
+    for name in ("scheduling_cost.png", "deadline_miss_rate.png"):
+        assert (tmp_path / "figures" / name).read_bytes().startswith(b"\x89PNG\r\n\x1a\n")
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 8f60ad5..0effed7 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -2,6 +2,10 @@
 import sys
 from pathlib import Path
 
+import pytest
+
+from adaptivequeue.cli import main
+
 
 def test_module_cli_displays_metrics() -> None:
     result = subprocess.run(
@@ -93,3 +97,21 @@ def test_cli_training_and_evaluation(tmp_path: Path) -> None:
     assert "Policy: qlearning | Episodes: 2" in evaluation.stdout
     assert "Primary cost J" in evaluation.stdout
     assert "95th-percentile waiting time" in evaluation.stdout
+
+
+def test_benchmark_cli_dispatch_preserves_output_directory(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture[str]
+) -> None:
+    from adaptivequeue import benchmark
+
+    called = []
+
+    def run(output_dir: Path, *, retrain: bool) -> list:
+        called.append((output_dir, retrain))
+        return []
+
+    monkeypatch.setattr(benchmark, "run_benchmark", run)
+    monkeypatch.setattr(sys, "argv", ["adaptivequeue", "benchmark", "--output-dir", str(tmp_path)])
+    main()
+    assert called == [(tmp_path, False)]
+    assert "per-episode results" in capsys.readouterr().out