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.

Game/scripts/policy_network.gd

@@ -14,9 +14,18 @@ extends RefCounted
 #     {"weights": [[out x in floats]], "biases": [out floats], "activation": "tanh" | "linear"},
 #     ...
 #   ]
+#   "action_space": {"type": "multi_discrete", "heads": [{"name","bins"}, ...]}  // optional
 # }
+#
+# "action_space" is absent from every model exported before curriculum
+# generation 4 (e.g. Game/bots/promoted/easy.json) — absence means
+# {"type": "continuous"}, decoded via ShipActionCodec.from_continuous, the
+# same flattened-Box(7)-mean-output path this class has always produced.
+# This class itself never changes behaviour based on it; only the caller
+# (AIShipController._decide) branches on action_space["type"].
 
 var input_size: int = 0
+var action_space: Dictionary = {"type": "continuous"}
 var _layers: Array = []
 
 
@@ -32,6 +41,7 @@ static func load_from_file(path: String) -> PolicyNetwork:
 
 	var net := PolicyNetwork.new()
 	net.input_size = int(data.get("input_size", 0))
+	net.action_space = data.get("action_space", {"type": "continuous"})
 	for layer in data["layers"]:
 		# Flatten each layer's weights into a PackedFloat64Array for speed
 		var out_size: int = layer["biases"].size()
@@ -55,7 +65,14 @@ static func load_from_file(path: String) -> PolicyNetwork:
 
 
 func forward(observation: Array) -> Array:
-	var x := PackedFloat64Array(observation)
+	if observation.size() < input_size:
+		push_error("PolicyNetwork: observation has %d values, model expects %d" % [observation.size(), input_size])
+	# Slice rather than trust the caller: ShipObservations.SIZE only ever
+	# grows (append-only), so an older/smaller model must still decode
+	# correctly against a newer, longer observation vector — the extra
+	# trailing values it never trained on are simply dropped here rather
+	# than corrupting the first layer's dot product by accident.
+	var x := PackedFloat64Array(observation.slice(0, input_size))
 	for layer in _layers:
 		var in_size: int = layer["in_size"]
 		var out_size: int = layer["out_size"]