feat(training): curriculum generation 4 — MultiDiscrete action space redesign

Three curriculum generations (2026-07-21 through 2026-08-04) all tried
gating *when* the policy could use vertical thrust/pitch-roll on top of a
continuous Gaussian action space, and all three failed the same way: PPO's
action-distribution std collapsed within ~10% of steps and never recovered,
landing at a 15-32% win rate vs the grounded reference regardless of
mechanism (hard mask, then a gradual ramp). Generation 3's final attempt
just landed at 24% — the worst of the three.

Root cause, verified against this project's own physics: hovering this ship
requires *holding* thrust.y ~= 0.408 continuously (mass 5.0, vertical_thrust
120, gravity 9.8). A collapsed near-zero-mean Gaussian can brush that value
but never sustain it long enough to earn the reward gradient that would
move the mean — no amount of gating *when* the axis acts fixes a problem in
*how* the policy represents a decision on it. This also independently found
and fixes a real bug: godot_rl never marks an episode timeout as a
truncation, so PPO was bootstrapping V(s)=0 on every 30s draw in every
generation to date.

- Game/scripts/ship_action_codec.gd (new): single source of truth for a
  per-axis MultiDiscrete action space (7 heads, nvec [5,5,5,5,5,5,2]) shared
  by training and in-game inference, replacing the continuous Gaussian.
  thrust_y's bins are deliberately asymmetric so a random policy drifts
  through the volume instead of floor-pinning. Legacy continuous decode
  (ai_ship_controller.gd's old logic) preserved verbatim so every
  pre-generation-4 export (e.g. Game/bots/promoted/easy.json) keeps working
  unchanged via an optional "action_space" JSON field.
- ship_observations.gd: append own contact state (SIZE 31 -> 35, append-only)
  so the value function can see what wall_contact_penalty fires on.
- ship_ai_controller.gd: action space/decode via the codec; drop the
  vertical_ramp/pitch_roll_ramp mechanism entirely; tilt_penalty default
  lowered 4x (aerial approaches require pitching); flight telemetry
  (airborne_fraction, mean_altitude, air_touch_fraction, vertical_thrust_mean)
  and truncation-snapshot fields on get_info().
- training_mode.gd: new air_drill_chance state-setter branch (ball spawned
  high, ships low, kept clear of walls) so aerial practice is forced by the
  environment instead of relying on reward-driven exploration alone; snapshot
  terminal observations before a timeout reset for the truncation fix.
- cosmic_env.py: remap ShipAIController's truncated/terminal_obs info into
  SB3's TimeLimit.truncated/terminal_observation keys.
- train.py: --reset-logits (+ --reset-logits-heads) replaces the
  now-meaningless --reset-std; new EntropyFloorCallback (a persistent
  per-rollout ent_coef controller replacing the one-shot std-reset shock)
  and per-head entropy logging; FlightTelemetryCallback; --air-drill-chance/
  --tilt-penalty flags; optional AbortIfCallback kill-criterion.
- export_policy.py: writes the action_space block for MultiDiscrete models;
  index-level parity check (argmax per head) instead of comparing floats.
- curriculum.py: full rewrite — 3 stages (bootstrap/selfplay/gauntlet), no
  grounded stage, full action space live from step 1; deletes generation
  1-3's checkpoint-lineage machinery (nothing to resume from); final report
  evaluates against both promoted/easy.json and the new
  promoted/reference-grounded.json (a copy of curric-s5-aggression, the
  strongest grounded-era artifact, kept as a fixed yardstick).
- run_training.sh/.gitignore: commit only final.zip, not the ~2400
  intermediate checkpoint files a single stage was writing (~500MB ->
  ~0.2MB per run); requirements.txt pinned (behaviour here now depends on
  specific library internals, not just public APIs).
- test_action_space.py (new): offline rung-0 check catching a head-order
  mismatch before it silently corrupts 24h of training.

Validated: GDScript compiles clean (Godot --headless --import + script
validation), free_play.tscn and training.tscn both boot headless without
errors, offline action-space assertions pass. Not yet run: the actual
smoke-training/A-B validation ladder steps in TRAINING.md's "Generation 4"
section, before committing to the full ~32h curriculum.

See TRAINING.md's "Generation 4" section for the full design writeup.

training/cosmic_env.py

@@ -10,6 +10,7 @@ learning policy: self-play by construction.
 import pathlib
 import subprocess
 
+import numpy as np
 from godot_rl.core.godot_env import GodotEnv
 from godot_rl.wrappers.stable_baselines_wrapper import StableBaselinesGodotEnv
 
@@ -72,8 +73,13 @@ class CosmicClashEnv(GodotEnv):
 class CosmicClashVecEnv(StableBaselinesGodotEnv):
     """SB3 VecEnv over N parallel CosmicClashEnv instances.
 
-    convert_action_space=True flattens the env's (Box(6), Discrete(2)) action
-    space into a single Box(7): thrust xyz, rotation xyz, turbo (>0 means on).
+    convert_action_space=True: godot_rl's ActionSpaceProcessor reports a
+    gym.spaces.MultiDiscrete when every per-axis action entry is Discrete
+    (see ShipActionCodec/ShipAIController.get_action_space) — nvec
+    [5,5,5,5,5,5,2] for rotation xyz, thrust xyz, turbo, in that
+    gymnasium-sorted key order. No conversion logic here needs to change for
+    that; this class's only functional addition is the truncation-info
+    remap below.
     """
 
     def __init__(self, godot_bin: str, n_parallel: int = 1, seed: int = 0, port: int = GodotEnv.DEFAULT_PORT, **kwargs):
@@ -90,3 +96,27 @@ class CosmicClashVecEnv(StableBaselinesGodotEnv):
         self.n_parallel = n_parallel
         self._check_valid_action_space()
         self.results = None
+
+    def step(self, action):
+        """Remap ShipAIController.get_info()'s "truncated"/"terminal_obs"
+        into the keys SB3's on_policy_algorithm looks for
+        ("TimeLimit.truncated"/"terminal_observation") so PPO bootstraps
+        V(s) through an episode timeout instead of treating every 30s draw
+        as a true terminal state.
+
+        Godot_rl's own godot_env.py never sets either key (it returns the
+        same `done` array for both term and trunc, "# TODO update API to
+        term, trunc") and StableBaselinesGodotEnv.step() only ever returns
+        that single collapsed `dones` array to SB3 — so without this, PPO
+        has no way to distinguish "episode ended because a goal was scored"
+        (a genuine terminal, V(s)=0 is correct) from "episode ended because
+        the 30s clock ran out" (an artificial boundary that should be
+        bootstrapped through), and was silently treating every draw as the
+        former in every curriculum generation to date.
+        """
+        obs, rewards, dones, infos = super().step(action)
+        for info in infos:
+            if info.pop("truncated", False):
+                info["TimeLimit.truncated"] = True
+                info["terminal_observation"] = {"obs": np.array(info.pop("terminal_obs"), dtype=np.float32)}
+        return obs, rewards, dones, infos