Document JAX PPO ladder results

README.md

@@ -233,9 +233,20 @@ opened_colors_mean=4.942626953125
 positive_expeditions_mean=0.41796875
 ```
 
+Static-opponent gate results, measured on 2026-07-04 with 10,000 fixed shuffles
+and duplicate seat-swapped evaluation:
+
+| Gate | Opponent | Result | Win rate (Wilson 95%) | Mean score diff | Mean length | Positive expeditions/game |
+| --- | --- | --- | --- | ---: | ---: | ---: |
+| 1 | `discard_only` | PASS | 1.00000 [0.99981, 1.00000] | 204.56335 | 82.45495 | 3.2783 |
+| 2 | `heuristic_balanced` | PASS | 0.98655 [0.98486, 0.98806] | 116.83400 | 167.81450 | 3.9573 |
+| 3 | `heuristic_cautious` | PASS | 0.95955 [0.95673, 0.96219] | 142.89930 | 185.38690 | 4.0946 |
+
 Large PPO artifacts are written under
 `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/`.
-Generated checkpoints and evaluation JSON are not committed to git.
+Generated checkpoints and evaluation JSON are not committed to git. The full
+run summary is in
+[docs/reports/jax-ppo-static-opponent-ladder-2026-07-04.md](docs/reports/jax-ppo-static-opponent-ladder-2026-07-04.md).
 
 ## Basic Usage
 

docs/plans/jax-ppo-static-opponents.md

@@ -1,6 +1,6 @@
 # Plan: JAX PPO Static-Opponent Ladder
 
-**Status:** Ready to implement.
+**Status:** Static-opponent ladder passed on 2026-07-04.
 **Owner:** Codex implements; operator reviews training gates.
 **Scope:** A compact PPO training stack on top of `lost_cities_jax`, using GPU
 via optional CUDA JAX execution.
@@ -68,7 +68,6 @@ Prefer a small number of files while keeping testable boundaries:
 src/lost_cities_jax/
   ppo.py          # config, network, rollout, PPO update, train loop
   opponents.py    # pure-JAX static opponent policy functions
-  eval.py         # duplicate evaluation and Wilson intervals
   ppo_cli.py      # argparse CLI
 configs/jax_ppo/
   discard-only.yaml
@@ -168,7 +167,7 @@ ppo:
   max_grad_norm: 0.5
   learning_rate: 0.0003
   epochs: 4
-  minibatches: 8
+  minibatches: 128
 reward:
   terminal_scale: 50.0
   potential_shaping_initial: 1.0
@@ -223,6 +222,45 @@ Gates:
 
 Only after all three gates pass should league self-play begin.
 
+## 2026-07-04 Gate Results
+
+All three static-opponent gates passed on an RTX 3090 using optional CUDA JAX:
+
+```bash
+uv run --with 'jax[cuda12]' lost-cities-jax-ppo train --config <config>
+uv run --with 'jax[cuda12]' lost-cities-jax-ppo eval --config <config> \
+  --checkpoint <run>/latest --games 10000 --duplicate --output <eval.json>
+```
+
+Training used `batch_games=8192`, `rollout_steps=400`, `total_updates=250`,
+`seed=20260704`, and artifact root
+`/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/`.
+The implementation commit used for the runs was `4c0c2e9`.
+
+| Gate | Opponent | Result | Win rate (Wilson 95%) | Mean score diff | Mean length | Positive expeditions/game |
+| --- | --- | --- | --- | ---: | ---: | ---: |
+| 1 | `discard_only` | PASS | 1.00000 [0.99981, 1.00000] | 204.56335 | 82.45495 | 3.2783 |
+| 2 | `heuristic_balanced` | PASS | 0.98655 [0.98486, 0.98806] | 116.83400 | 167.81450 | 3.9573 |
+| 3 | `heuristic_cautious` | PASS | 0.95955 [0.95673, 0.96219] | 142.89930 | 185.38690 | 4.0946 |
+
+Artifact directories:
+
+- Gate 1: `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_223401_jax-ppo-discard-only/`
+- Gate 2: `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_224749_jax-ppo-balanced/`
+- Gate 3: `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_230150_jax-ppo-cautious/`
+
+Final training rollout canaries:
+
+| Opponent | Final train return_mean | Final play_action_rate | Final max_steps_rate |
+| --- | ---: | ---: | ---: |
+| `discard_only` | 0.99281 | 0.59007 | 0.00000 |
+| `heuristic_balanced` | 0.93545 | 0.37737 | 0.00415 |
+| `heuristic_cautious` | 0.97101 | 0.37473 | 0.00391 |
+
+The balanced and cautious policies win decisively but produce longer games than
+the discard-only diagnostic. That is not a gate failure, but the next training
+phase should keep game length and forced-end rate as canaries.
+
 ## Artifact Policy
 
 Generated training output stays out of git. Use:
@@ -313,10 +351,10 @@ tmux new-session -s coolrl-jax-ppo-discard \
 Duplicate evaluation:
 
 ```bash
-flock -n .compute.lock uv run --with 'jax[cuda12]' lost-cities-jax-ppo eval \
+uv run --with 'jax[cuda12]' lost-cities-jax-ppo eval \
+  --config configs/jax_ppo/discard-only.yaml \
   --checkpoint /mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/<run>/latest \
   --opponent discard_only \
-  --shuffle-bank-seed 20260704 \
   --games 10000 \
   --duplicate \
   --output /mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/<run>/eval_duplicate.json
@@ -336,5 +374,5 @@ flock -n .compute.lock uv run --with 'jax[cuda12]' lost-cities-jax-ppo eval \
 
 ## Current Next Action
 
-Implement Phase 1: static opponents plus random-policy batched rollout with the
+The static-opponent ladder is complete. The next eligible phase is snapshot-pool
-full dashboard, then record the random baseline before adding PPO updates.
+league self-play design, using the three passed checkpoints as initial anchors.

docs/reports/jax-ppo-static-opponent-ladder-2026-07-04.md

@@ -0,0 +1,68 @@
+# JAX PPO Static-Opponent Ladder Report - 2026-07-04
+
+**Status:** PASS.
+**Implementation commit:** `4c0c2e9`.
+**Hardware:** RTX 3090 with optional CUDA JAX via `uv run --with 'jax[cuda12]'`.
+**Artifact root:** `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/`.
+
+## Protocol
+
+Each opponent was trained from scratch with the same PPO configuration:
+
+- `batch_games=8192`
+- `rollout_steps=400`
+- `total_updates=250`
+- `gamma=1.0`
+- `gae_lambda=0.95`
+- `clip_epsilon=0.2`
+- `entropy_coef=0.01`
+- `seed=20260704`
+
+Evaluation used a fixed 10,000-deck shuffle bank and duplicate play: each deck
+was played once with the learner in seat 0 and once with the learner in seat 1,
+for 20,000 evaluated games per gate. Win rates include Wilson 95% intervals.
+
+## Results
+
+| Gate | Opponent | Result | Win rate (Wilson 95%) | Mean score diff | Mean game length | Positive expeditions/game |
+| --- | --- | --- | --- | ---: | ---: | ---: |
+| 1 | `discard_only` | PASS | 1.00000 [0.99981, 1.00000] | 204.56335 | 82.45495 | 3.2783 |
+| 2 | `heuristic_balanced` | PASS | 0.98655 [0.98486, 0.98806] | 116.83400 | 167.81450 | 3.9573 |
+| 3 | `heuristic_cautious` | PASS | 0.95955 [0.95673, 0.96219] | 142.89930 | 185.38690 | 4.0946 |
+
+Gate 1 passed both required conditions: win rate >= 90% and at least two
+positive expeditions per game. Gates 2 and 3 passed their mean-score-difference
+condition.
+
+## Artifacts
+
+| Opponent | Run directory | Evaluation JSON |
+| --- | --- | --- |
+| `discard_only` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_223401_jax-ppo-discard-only/` | `eval_discard_only_duplicate.json` |
+| `heuristic_balanced` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_224749_jax-ppo-balanced/` | `eval_balanced_duplicate.json` |
+| `heuristic_cautious` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/2026-07-04_230150_jax-ppo-cautious/` | `eval_cautious_duplicate.json` |
+
+The random rollout baseline against `discard_only` is stored at:
+
+```text
+/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/random_baseline_discard_only.json
+```
+
+## Canary Notes
+
+The random-policy baseline against `discard_only` had `play_action_rate=0.28855`
+and only `0.41797` positive expeditions per game. The trained discard-only
+policy ended duplicate evaluation at `play_action_rate=0.64598` and `3.2783`
+positive expeditions per game, so the intended anti-collapse signal is present.
+
+Balanced and cautious checkpoints pass their score gates decisively, but their
+duplicate evaluations are longer: mean game lengths are `167.81450` and
+`185.38690`. The final training rollout forced-end rates were low but nonzero
+for those opponents (`0.00415` and `0.00391`). Treat game length and forced-end
+rate as canaries in the next self-play phase.
+
+## Next Step
+
+The static ladder has cleared. The next phase can start snapshot-pool league
+self-play, using these three passed checkpoints as initial anchors and keeping
+duplicate evaluation as the regression gate.

docs/research/deep-cfr-v0-gap-vs-coolrl.md

@@ -14,7 +14,7 @@ The core algorithmic components have been ported to Cython to ensure C-level per
 - `src/coolrl_lost_cities/games/classic/game.pyx:217`: `cdef class GameState` provides high-speed state mutation, legal action generation, and scoring.
 - `src/coolrl_lost_cities/games/classic/deep_cfr/traversal.pyx:228`: `cpdef traverse` serves as the entry point for the recursive Deep CFR traversal engine.
 - `src/coolrl_lost_cities/games/classic/deep_cfr/traversal.pyx:253`: `cdef _traverse` implements the core recursive tree-walking logic, including traverser/opponent node handling and outcome sampling.
-- `src/coolrl_lost_cities/games/classic/deep_cfr/encoding.pyx:425`: `def encode_info_state` generates the information-state feature vectors required for network inference.
+- `src/coolrl_lost_cities/games/classic/deep_cfr/encoding.pyx:406`: `def encode_info_state` generates the information-state feature vectors required for network inference.
 - `src/coolrl_lost_cities/games/classic/deep_cfr/cfr_math.pyx`: Contains optimized regret-matching and advantage calculation primitives.
 
 ## Analysis