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chore(training): retune stage-4 handling penalties and restart from Stage-3 foundation

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Stage 4's upright/forward-motion telemetry plateaued flat across all three
blocked attempts because ground_tilt_penalty (0.003) was too weak to matter
and nothing penalized sideways/reverse motion at all. Raise
ground_tilt_penalty to 0.05 and add a new non_forward_penalty term
(ship_ai_controller.gd) that directly costs non-forward planar velocity near
the floor, independent of the ball. Delete the three blocked attempts'
checkpoints/logs/exports and reset generation5_state.json so the next run
starts fresh from the Stage-3 foundation checkpoint instead of continuing
from the drifted retry2 weights.
This commit is contained in:
Josh Creek
2026-08-09 13:09:12 +01:00
parent 005cd0c66e
commit c56f5ed1a3
14 changed files with 41 additions and 263 deletions
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Game/bots/20260808-1508-gen5-s4-handling.json
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Game/bots/20260808-2120-gen5-s4-handling-retry1.json
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Game/bots/20260809-0328-gen5-s4-handling-retry2.json
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Game/scripts/ship_ai_controller.gd
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@@ -74,6 +74,11 @@ 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 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 +329,20 @@ 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.
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 = maxf(non_forward_planar_velocity.dot(non_forward_planar_forward.normalized()), 0.0)
var non_forward_speed: float = non_forward_planar_speed - forward_component
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 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/training_mode.gd
+2 -1
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@@ -251,7 +251,7 @@ 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",
"ground_tilt_penalty", "non_forward_penalty", "speed_reward_weight", "time_penalty",
"airborne_penalty",
]
@@ -312,6 +312,7 @@ 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
"speed_reward_weight": return 0.004
"time_penalty": return 0.001
"airborne_penalty": return 0.0
training/checkpoints/20260808-1508-gen5-s4-handling/final.zip
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training/checkpoints/20260808-2120-gen5-s4-handling-retry1/final.zip
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training/checkpoints/20260809-0328-gen5-s4-handling-retry2/final.zip
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training/generation5.py
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@@ -43,7 +43,15 @@ 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.
HANDLING_REWARD_FLAGS = [
"--velocity-to-ball-weight", "0.04",
"--forward-velocity-to-ball-weight", "0.06",
@@ -53,7 +61,8 @@ 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",
]
STAGES = [
training/generation5_state.json
+3 -257
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@@ -1,260 +1,6 @@
{
"stage_index": 0,
"attempt": 2,
"status": "blocked",
"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"
},
{
"stage_index": 0,
"stage_number": 4,
"stage_name": "handling",
"experiment": "20260809-0328-gen5-s4-handling-retry2",
"attempt": 2,
"telemetry_tail": {
"rollout/air_touch_fraction": 0.0002799999937415123,
"rollout/airborne_fraction": 0.4334809045791626,
"rollout/ep_len_mean": 136.65584008789062,
"rollout/ep_rew_mean": 7.696315350532532,
"rollout/forward_motion_fraction": 0.23557147654891014,
"rollout/goal_rate": 0.5825200036764145,
"rollout/mean_altitude": 4.346557500362397,
"rollout/productive_air_touch_fraction": 3.999999910593033e-05,
"rollout/upright_fraction": 0.2288729049563408,
"rollout/vertical_thrust_mean": 0.06853299860764295
},
"telemetry_failures": [
"rollout/goal_rate=0.5825 < 0.8000",
"rollout/upright_fraction=0.2289 < 0.4500",
"rollout/forward_motion_fraction=0.2356 < 0.2500"
],
"evaluation_goal_failures": [
"easy.json: goal_rate=0.750 < 0.800"
],
"side_balance_failures": [],
"eval": {
"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
},
"evals": [
{
"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
}
],
"decision": "fail"
}
]
"attempt": 0,
"status": "in_progress",
"log": []
}
training/logs/20260808-1508-gen5-s4-handling_0/events.out.tfevents.1786198156.ai.362254.0
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training/logs/20260808-2120-gen5-s4-handling-retry1_0/events.out.tfevents.1786220434.ai.526119.0
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training/logs/20260809-0328-gen5-s4-handling-retry2_0/events.out.tfevents.1786242553.ai.688330.0
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training/train.py
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@@ -358,6 +358,11 @@ 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(
"--speed-reward-weight", type=float, default=None,
help="Overrides the orientation-agnostic own-speed reward (generation 5 handling sets it to zero)",
@@ -391,6 +396,7 @@ 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_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,