chore(training): add air_approach_weight and restart stage-5 intercepts

Stage 5 blocked all three attempts on productive_air_touch_fraction
stuck exactly at 0.0 across a continuous 180M-step lineage, while
goal_rate/upright_fraction/forward_motion_fraction kept improving on
the same budget. forward_velocity_to_ball_weight (the term that solved
Stage 4's ground pursuit) is hard-gated below GROUND_HANDLING_HEIGHT
and does nothing in the air, so Stage 5's air_intercept_chance had no
matching aerial incentive to learn from. air_approach_weight adds the
airborne mirror (nose-first 3D closing speed, no uprightness
multiplier) and folds into HANDLING_REWARD_FLAGS so Stage 6 inherits
it too. Deleted the three blocked attempts and reset state to resume
Stage 5 from the Stage-4 checkpoint with the new term.

training/train.py

@@ -355,6 +355,11 @@ def parse_args():
         "--forward-velocity-to-ball-weight", type=float, default=None,
         help="Low-altitude dense reward for nose-led planar approach toward the ball",
     )
+    curriculum.add_argument(
+        "--air-approach-weight", type=float, default=None,
+        help="Aerial mirror of forward-velocity-to-ball-weight: high-altitude dense reward for "
+        "nose-led 3D closing speed toward the ball",
+    )
     curriculum.add_argument(
         "--ball-distance-penalty", type=float, default=None,
         help="Overrides ShipAIController.ball_distance_penalty (dense per-tick cost scaled by distance to the ball)",
@@ -419,6 +424,7 @@ def _curriculum_kwargs(args) -> dict:
         "ai_grounded_upright_reward": args.grounded_upright_reward,
         "ai_velocity_to_ball_weight": args.velocity_to_ball_weight,
         "ai_forward_velocity_to_ball_weight": args.forward_velocity_to_ball_weight,
+        "ai_air_approach_weight": args.air_approach_weight,
         "ai_ball_distance_penalty": args.ball_distance_penalty,
         "ai_ball_touch_reward": args.ball_touch_reward,
         "ai_airborne_penalty": args.airborne_penalty,