fix(training): correct non-forward penalty math and add a grounding incentive

Adversarial review of the previous stage-4 retune found two problems:
non_forward_speed used planar_speed - forward_component, which under-charges
diagonal motion relative to true lateral speed (e.g. ~29% penalty at 45
degrees off the nose instead of the correct ~71%); fixed to the Pythagorean
magnitude for forward-facing angles, full speed for backward-facing ones.

Also, ground_tilt_penalty and non_forward_penalty only ever cost reward near
the floor with nothing offsetting them above it, which could teach a policy
that's still bad at ground handling to just avoid the floor rather than get
better at it. Added grounded_upright_reward (ship_ai_controller.gd) plus a
new ShipObservations.is_floor_contact helper for genuine belly-on-floor
contact detection, so grounding well while upright is the locally profitable
choice, not just the least-punished one.

Game/scripts/ship_ai_controller.gd

@@ -74,6 +74,25 @@ extends AIController3D
 # pitch/roll during a real aerial. Generation 5 uses this instead of raising
 # the global tilt_penalty back to its pre-flight value.
 @export var ground_tilt_penalty := 0.0
+# Per-tick penalty on the planar-velocity component not pointed along the
+# nose (sideways or reverse), independent of the ball — the mirror image of
+# forward_velocity_to_ball_weight's ball-conditioned bonus. Same
+# GROUND_HANDLING_HEIGHT altitude fade as ground_tilt_penalty.
+@export var non_forward_penalty := 0.0
+# Per-tick bonus for genuinely resting on the floor (ShipObservations.
+# is_floor_contact, real contact — not just being below
+# GROUND_HANDLING_HEIGHT) while upright. The positive counterpart to
+# ground_tilt_penalty/non_forward_penalty: without it, staying above
+# GROUND_HANDLING_HEIGHT is reward-neutral relative to grounding, so a
+# policy that's still bad at ground handling could "solve" those penalties
+# by just avoiding the floor rather than by getting better at handling on
+# it — worsening Stage 3's already-airborne-heavy baseline instead of
+# fixing it. Kept an order of magnitude below ball_touch_reward/goal_reward
+# and comparable to time_penalty/ball_distance_penalty so grounding well is
+# attractive without making idling upright on the spot, away from the ball,
+# competitive with actually playing (see ball_distance_penalty's run04
+# lesson on why a flat positional bonus needs a countervailing cost).
+@export var grounded_upright_reward := 0.0
 # Per-tick bonus for own speed: 0 stationary, full value (+0.24/s) at
 # max_speed. Run07 lesson: after the kickoff flurry both ships parked next to
 # a cornered ball — with every other dense term near zero there, standing
@@ -324,6 +343,40 @@ func _physics_process(delta):
 		var tilt_ground_factor: float = 1.0 - clampf(ship.global_position.y / GROUND_HANDLING_HEIGHT, 0.0, 1.0)
 		reward -= ground_tilt_penalty * (1.0 - ground_uprightness) * 0.5 * tilt_ground_factor
 
+	# Dense penalty: any planar velocity component not pointed along the nose
+	# (sideways or reverse), independent of the ball — the mirror image of
+	# forward_velocity_to_ball_weight's ball-conditioned bonus. Fades out with
+	# altitude via the same GROUND_HANDLING_HEIGHT ramp as ground_tilt_penalty.
+	# non_forward_speed is the true lateral magnitude (Pythagorean, not the
+	# cruder planar_speed - forward_component, which under-charges diagonal
+	# motion — e.g. at 45 degrees off the nose that gave ~29% of full-speed
+	# penalty instead of the correct ~71%) for any forward-facing component;
+	# a backward-facing component (dot product below zero) is fully
+	# penalized regardless of angle, same as pure sideways motion.
+	if non_forward_penalty > 0.0 and ship.global_position.y < GROUND_HANDLING_HEIGHT:
+		var non_forward_planar_velocity := Vector3(ship.linear_velocity.x, 0.0, ship.linear_velocity.z)
+		var non_forward_planar_speed := non_forward_planar_velocity.length()
+		var non_forward_planar_forward := Vector3(-ship.global_transform.basis.z.x, 0.0, -ship.global_transform.basis.z.z)
+		if non_forward_planar_speed > 0.0001 and non_forward_planar_forward.length_squared() > 0.0001:
+			var forward_component: float = non_forward_planar_velocity.dot(non_forward_planar_forward.normalized())
+			var non_forward_speed: float
+			if forward_component >= 0.0:
+				non_forward_speed = sqrt(maxf(
+					non_forward_planar_speed * non_forward_planar_speed - forward_component * forward_component, 0.0
+				))
+			else:
+				non_forward_speed = non_forward_planar_speed
+			var non_forward_ground_factor: float = 1.0 - clampf(ship.global_position.y / GROUND_HANDLING_HEIGHT, 0.0, 1.0)
+			reward -= non_forward_penalty * (non_forward_speed / ship.max_speed) * non_forward_ground_factor
+
+	# Dense bonus: genuinely resting on the floor while upright (see
+	# grounded_upright_reward) — the positive counterpart to
+	# ground_tilt_penalty/non_forward_penalty, so grounding is worth
+	# pursuing, not just less punished than staying airborne.
+	if grounded_upright_reward > 0.0 and ShipObservations.is_floor_contact(ship):
+		var grounded_uprightness: float = ship.global_transform.basis.y.dot(Vector3.UP)
+		reward += grounded_upright_reward * maxf(grounded_uprightness, 0.0)
+
 	# Dense penalty: height above the floor (see airborne_penalty). The
 	# floor sits at world y = 0 (see training_mode.gd's FIELD_MIN_Y/
 	# _escaped bounds); normalized so the worst case is pinned at the

Game/scripts/ship_observations.gd

@@ -143,3 +143,21 @@ static func contact_normal(ship: Ship) -> Vector3:
 		if normal.y < FLOOR_NORMAL_MIN_Y:
 			return normal
 	return Vector3.ZERO
+
+
+# True belly-on-floor contact — the complement of contact_normal, which
+# deliberately excludes floor contact (see its comment). Used by
+# ShipAIController.grounded_upright_reward to reward genuinely resting on
+# the floor rather than just being below the GROUND_HANDLING_HEIGHT proxy
+# altitude, so a ship can't collect ground-handling reward by hovering just
+# under the threshold without ever touching down.
+static func is_floor_contact(ship: Ship) -> bool:
+	var state := PhysicsServer3D.body_get_direct_state(ship.get_rid())
+	if state == null:
+		return false
+	for i in state.get_contact_count():
+		if not state.get_contact_collider_object(i) is ArenaBoundary:
+			continue
+		if state.get_contact_local_normal(i).y >= FLOOR_NORMAL_MIN_Y:
+			return true
+	return false

Game/scripts/training_mode.gd

@@ -251,8 +251,8 @@ const SHIP_AI_OVERRIDES := [
 	"ball_touch_reward", "ball_touch_cooldown_ticks", "ball_touch_direction_floor",
 	"velocity_to_ball_weight", "ball_velocity_to_goal_weight", "ball_distance_penalty",
 	"forward_velocity_to_ball_weight", "wall_contact_penalty", "tilt_penalty",
-	"ground_tilt_penalty", "speed_reward_weight", "time_penalty",
-	"airborne_penalty",
+	"ground_tilt_penalty", "non_forward_penalty", "grounded_upright_reward",
+	"speed_reward_weight", "time_penalty", "airborne_penalty",
 ]
 
 
@@ -312,6 +312,8 @@ func _ai_default(name: String) -> Variant:
 		"wall_contact_penalty": return 0.0025
 		"tilt_penalty": return 0.0005
 		"ground_tilt_penalty": return 0.0
+		"non_forward_penalty": return 0.0
+		"grounded_upright_reward": return 0.0
 		"speed_reward_weight": return 0.004
 		"time_penalty": return 0.001
 		"airborne_penalty": return 0.0

training/eval_history.json

@@ -252,5 +252,32 @@
       "team_1": 46
     },
     "win_rate_a": 0.43
+  },
+  {
+    "timestamp": "2026-08-09T08:36:44+00:00",
+    "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260809-0328-gen5-s4-handling-retry2.json",
+    "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/promoted/easy.json",
+    "seed": 1,
+    "episodes": 100,
+    "wins_a": 46,
+    "wins_b": 29,
+    "draws": 25,
+    "side_results": {
+      "a_team_0": {
+        "wins_a": 21,
+        "wins_b": 18,
+        "draws": 11
+      },
+      "a_team_1": {
+        "wins_a": 25,
+        "wins_b": 11,
+        "draws": 14
+      }
+    },
+    "physical_team_wins": {
+      "team_0": 32,
+      "team_1": 43
+    },
+    "win_rate_a": 0.46
   }
 ]

training/generation5.py

@@ -43,7 +43,21 @@ STANDING_ARGS = ["--ent-coef", "0.01", "--entropy-floor"]
 # Scoring/ball-direction shaping inherited from generation 4. Handling
 # replaces half the orientation-agnostic closing reward and all generic speed
 # reward with nose-led ground approach, while keeping global tilt pressure
-# small enough for flight and adding a stronger floor-local term.
+# small enough for flight. The first three Stage-4 attempts (2026-08-08/09)
+# plateaued with upright_fraction/forward_motion_fraction flat at ~0.22-0.26
+# against 0.45/0.25 floors for 120M cumulative timesteps: ground_tilt_penalty
+# at 0.003 only cost a fully-sideways episode ~2.7 reward, trivial next to a
+# goal (80) or a touch (0.7). ground_tilt_penalty is raised ~17x to 0.05 (a
+# full sideways episode now costs ~45, comparable to a goal) and
+# non_forward_penalty is a new term (ship_ai_controller.gd) directly costing
+# sideways/reverse planar velocity near the floor, independent of the ball,
+# since nothing previously penalized that at all. Both are floor-proximity
+# penalties only, with nothing equivalent above GROUND_HANDLING_HEIGHT — on
+# its own that risks teaching "avoid the floor" instead of "handle well on
+# it", worsening Stage 3's already-airborne-heavy baseline. grounded_upright_
+# reward is the positive counterpart: a bonus for genuine floor contact
+# (not just low altitude) while upright, so grounding well is the locally
+# profitable choice rather than merely the least-punished one.
 HANDLING_REWARD_FLAGS = [
     "--velocity-to-ball-weight", "0.04",
     "--forward-velocity-to-ball-weight", "0.06",
@@ -53,7 +67,9 @@ HANDLING_REWARD_FLAGS = [
     "--goal-reward", "80",
     "--speed-reward-weight", "0.0",
     "--tilt-penalty", "0.0002",
-    "--ground-tilt-penalty", "0.003",
+    "--ground-tilt-penalty", "0.05",
+    "--non-forward-penalty", "0.04",
+    "--grounded-upright-reward", "0.015",
 ]
 
 STAGES = [

training/generation5_state.json

@@ -1,175 +1,6 @@
 {
   "stage_index": 0,
-  "attempt": 2,
+  "attempt": 0,
   "status": "in_progress",
-  "log": [
-    {
-      "stage_index": 0,
-      "stage_number": 4,
-      "stage_name": "handling",
-      "experiment": "20260808-1508-gen5-s4-handling",
-      "attempt": 0,
-      "telemetry_tail": {
-        "rollout/air_touch_fraction": 0.0004599999897181988,
-        "rollout/airborne_fraction": 0.42232042902708056,
-        "rollout/ep_len_mean": 137.51244003295898,
-        "rollout/ep_rew_mean": 6.987018242835998,
-        "rollout/forward_motion_fraction": 0.22800671431422234,
-        "rollout/goal_rate": 0.5818800025582314,
-        "rollout/mean_altitude": 4.261052157402038,
-        "rollout/productive_air_touch_fraction": 7.999999821186065e-05,
-        "rollout/upright_fraction": 0.22364509524405002,
-        "rollout/vertical_thrust_mean": 0.09885899936710485
-      },
-      "telemetry_failures": [
-        "rollout/goal_rate=0.5819 < 0.8000",
-        "rollout/upright_fraction=0.2236 < 0.4500",
-        "rollout/forward_motion_fraction=0.2280 < 0.2500"
-      ],
-      "evaluation_goal_failures": [
-        "easy.json: goal_rate=0.750 < 0.800"
-      ],
-      "side_balance_failures": [],
-      "eval": {
-        "timestamp": "2026-08-08T20:20:09+00:00",
-        "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260808-1508-gen5-s4-handling.json",
-        "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/promoted/easy.json",
-        "seed": 1,
-        "episodes": 100,
-        "wins_a": 45,
-        "wins_b": 30,
-        "draws": 25,
-        "side_results": {
-          "a_team_0": {
-            "wins_a": 18,
-            "wins_b": 19,
-            "draws": 13
-          },
-          "a_team_1": {
-            "wins_a": 27,
-            "wins_b": 11,
-            "draws": 12
-          }
-        },
-        "physical_team_wins": {
-          "team_0": 29,
-          "team_1": 46
-        },
-        "win_rate_a": 0.45
-      },
-      "evals": [
-        {
-          "timestamp": "2026-08-08T20:20:09+00:00",
-          "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260808-1508-gen5-s4-handling.json",
-          "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/promoted/easy.json",
-          "seed": 1,
-          "episodes": 100,
-          "wins_a": 45,
-          "wins_b": 30,
-          "draws": 25,
-          "side_results": {
-            "a_team_0": {
-              "wins_a": 18,
-              "wins_b": 19,
-              "draws": 13
-            },
-            "a_team_1": {
-              "wins_a": 27,
-              "wins_b": 11,
-              "draws": 12
-            }
-          },
-          "physical_team_wins": {
-            "team_0": 29,
-            "team_1": 46
-          },
-          "win_rate_a": 0.45
-        }
-      ],
-      "decision": "fail"
-    },
-    {
-      "stage_index": 0,
-      "stage_number": 4,
-      "stage_name": "handling",
-      "experiment": "20260808-2120-gen5-s4-handling-retry1",
-      "attempt": 1,
-      "telemetry_tail": {
-        "rollout/air_touch_fraction": 0.00023999999463558198,
-        "rollout/airborne_fraction": 0.4264982384443283,
-        "rollout/ep_len_mean": 135.5532396697998,
-        "rollout/ep_rew_mean": 7.420562901496887,
-        "rollout/forward_motion_fraction": 0.2443552376627922,
-        "rollout/goal_rate": 0.5880400028824806,
-        "rollout/mean_altitude": 4.2706854009628294,
-        "rollout/productive_air_touch_fraction": 0.0,
-        "rollout/upright_fraction": 0.2381512857079506,
-        "rollout/vertical_thrust_mean": 0.07293799882452004
-      },
-      "telemetry_failures": [
-        "rollout/goal_rate=0.5880 < 0.8000",
-        "rollout/upright_fraction=0.2382 < 0.4500",
-        "rollout/forward_motion_fraction=0.2444 < 0.2500"
-      ],
-      "evaluation_goal_failures": [],
-      "side_balance_failures": [],
-      "eval": {
-        "timestamp": "2026-08-09T02:28:48+00:00",
-        "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260808-2120-gen5-s4-handling-retry1.json",
-        "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/promoted/easy.json",
-        "seed": 1,
-        "episodes": 100,
-        "wins_a": 43,
-        "wins_b": 38,
-        "draws": 19,
-        "side_results": {
-          "a_team_0": {
-            "wins_a": 18,
-            "wins_b": 21,
-            "draws": 11
-          },
-          "a_team_1": {
-            "wins_a": 25,
-            "wins_b": 17,
-            "draws": 8
-          }
-        },
-        "physical_team_wins": {
-          "team_0": 35,
-          "team_1": 46
-        },
-        "win_rate_a": 0.43
-      },
-      "evals": [
-        {
-          "timestamp": "2026-08-09T02:28:48+00:00",
-          "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260808-2120-gen5-s4-handling-retry1.json",
-          "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/promoted/easy.json",
-          "seed": 1,
-          "episodes": 100,
-          "wins_a": 43,
-          "wins_b": 38,
-          "draws": 19,
-          "side_results": {
-            "a_team_0": {
-              "wins_a": 18,
-              "wins_b": 21,
-              "draws": 11
-            },
-            "a_team_1": {
-              "wins_a": 25,
-              "wins_b": 17,
-              "draws": 8
-            }
-          },
-          "physical_team_wins": {
-            "team_0": 35,
-            "team_1": 46
-          },
-          "win_rate_a": 0.43
-        }
-      ],
-      "decision": "fail"
-    }
-  ]
+  "log": []
 }

training/train.py

@@ -358,6 +358,16 @@ def parse_args():
         "--ground-tilt-penalty", type=float, default=None,
         help="Low-altitude-only tilt cost that fades to zero by the handling-height threshold",
     )
+    curriculum.add_argument(
+        "--non-forward-penalty", type=float, default=None,
+        help="Overrides ShipAIController.non_forward_penalty (low-altitude dense cost on sideways/reverse "
+        "planar velocity, independent of the ball)",
+    )
+    curriculum.add_argument(
+        "--grounded-upright-reward", type=float, default=None,
+        help="Overrides ShipAIController.grounded_upright_reward (dense bonus for genuine floor contact "
+        "while upright, countering an incentive to just avoid the floor)",
+    )
     curriculum.add_argument(
         "--speed-reward-weight", type=float, default=None,
         help="Overrides the orientation-agnostic own-speed reward (generation 5 handling sets it to zero)",
@@ -391,6 +401,8 @@ def _curriculum_kwargs(args) -> dict:
         "team_size": args.team_size,
         "ai_tilt_penalty": args.tilt_penalty,
         "ai_ground_tilt_penalty": args.ground_tilt_penalty,
+        "ai_non_forward_penalty": args.non_forward_penalty,
+        "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_ball_distance_penalty": args.ball_distance_penalty,