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chore(training): add air_touch_bonus_weight and restart stage-5 intercepts
air_approach_weight alone didn't move productive_air_touch_fraction after a further 180M steps (360M cumulative across all six Stage-5 attempts): an unredirected air-intercept ball falls short of the goal from gravity and just lands on the floor, so the already-solved ground game collects the same episode reward whether or not anything touched the ball in the air. air_touch_bonus_weight adds a conjunctive event bonus on top of ball_touch_reward for a touch that's both genuinely aerial and goal-directed, targeting the actual measured behaviour instead of only the approach to it.
This commit is contained in:
Josh Creek
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@@ -60,6 +60,29 @@ extends AIController3D
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# vector rather than the planar-only one, since a real aerial requires
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# pitching away from level.
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@export var air_approach_weight := 0.0
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# Event bonus, conjunctive with the same goal-direction alignment already
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# gating ball_touch_reward: extra payout for a touch that is BOTH genuinely
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# aerial (ball.y > AIR_TOUCH_HEIGHT, matching the productive_air_touch_
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# fraction telemetry definition exactly) AND goal-directed. air_approach_
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# weight alone did not move productive_air_touch_fraction (still 0.0 after
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# a further 180M steps, 360M cumulative) because nothing in the reward ever
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# made touching the ball while still airborne worth more than the
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# alternative every policy already had available for free: let gravity pull
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# an unredirected air-intercept ball back down (it falls well short of the
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# goal's ~0-1.5m height band over the required flight distance, so it does
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# not auto-score) and then collect the same goal_reward/ball_touch_reward
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# via the already-dominant, already-solved ground game once it lands. This
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# is deliberately NOT the standalone height-only bonus generation 4 ruled
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# out (see TRAINING.md's "why no air-touch reward" note, added to close the
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# RLGym wall-bounce exploit): it only pays scaled by the same alignment
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# dot-product as the base term, so batting the ball in a non-productive
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# direction earns nothing extra, same anti-farming shape as ball_touch_
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# reward itself. Also safe from that specific exploit on distributional
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# grounds: air-intercept spawns are central (x in [-8,8], z in [-10,10],
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# arena half-extents 18/27) and air-drill spawns keep AIR_DRILL_BALL_WALL_
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# CLEARANCE from every wall, so neither state can be solved by bouncing off
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# one.
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@export var air_touch_bonus_weight := 0.0
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@export var ball_velocity_to_goal_weight := 0.004
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# Per-tick penalty scaled by distance to the ball (full value at the arena's
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# far diagonal, 0 on top of the ball). Run04 lesson: with idling worth a flat
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@@ -504,14 +527,16 @@ func _on_ship_body_entered(body: Node) -> void:
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var to_goal := attack_goal_position - ball.global_position
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if to_goal.length_squared() > 0.0001 and ball.linear_velocity.length_squared() > 0.0001:
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alignment = clampf(ball.linear_velocity.normalized().dot(to_goal.normalized()), 0.0, 1.0)
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reward += ball_touch_reward * lerpf(ball_touch_direction_floor, 1.0, alignment)
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var touch_payout := ball_touch_reward * lerpf(ball_touch_direction_floor, 1.0, alignment)
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if air_touch_bonus_weight > 0.0 and ball.global_position.y > AIR_TOUCH_HEIGHT:
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touch_payout += air_touch_bonus_weight * alignment
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reward += touch_payout
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_ticks_since_ball_touch = 0
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# Telemetry only (see get_info's air_touch_fraction) — not a reward term.
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# Generation 4 deliberately doesn't reward high touches directly (see
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# TRAINING.md's "why no air-touch reward" note); this just measures
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# whether the air-drill state setter is producing genuine aerial
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# contests, so a future decision to add one is data-driven.
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# air_touch_fraction/productive_air_touch_fraction (see get_info) share
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# their AIR_TOUCH_HEIGHT/alignment definitions 1:1 with air_touch_bonus_
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# weight above by design — the reward now targets exactly the behaviour
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# the telemetry measures.
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_touches += 1
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if ball.global_position.y > AIR_TOUCH_HEIGHT:
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_air_touches += 1
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@@ -265,7 +265,7 @@ const TRAINING_MODE_OVERRIDES := [
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const SHIP_AI_OVERRIDES := [
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"ball_touch_reward", "ball_touch_cooldown_ticks", "ball_touch_direction_floor",
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"velocity_to_ball_weight", "ball_velocity_to_goal_weight", "ball_distance_penalty",
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"forward_velocity_to_ball_weight", "air_approach_weight", "wall_contact_penalty", "tilt_penalty",
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"forward_velocity_to_ball_weight", "air_approach_weight", "air_touch_bonus_weight", "wall_contact_penalty", "tilt_penalty",
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"ground_tilt_penalty", "non_forward_penalty", "grounded_upright_reward",
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"speed_reward_weight", "time_penalty", "airborne_penalty",
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]
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@@ -324,6 +324,7 @@ func _ai_default(name: String) -> Variant:
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"velocity_to_ball_weight": return 0.02
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"forward_velocity_to_ball_weight": return 0.0
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"air_approach_weight": return 0.0
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"air_touch_bonus_weight": return 0.0
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"ball_velocity_to_goal_weight": return 0.004
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"ball_distance_penalty": return 0.002
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"wall_contact_penalty": return 0.0025
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+28
@@ -662,6 +662,34 @@ were deleted and Stage 5 restarts from Stage 4's checkpoint with the new term,
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same reasoning as every previous mechanism change: don't resume a policy
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shaped by an absent term into one where it now exists.
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**`air_approach_weight` alone did not fix it.** A further three attempts
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(180M more steps, 360M cumulative across all six Stage-5 attempts, 2026-08-18
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to 2026-08-19) closed with `productive_air_touch_fraction` still exactly 0.0
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and `air_touch_fraction` still at noise level, while `goal_rate` kept clearing
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its (lower) floor. Working out the actual physics instead of retuning another
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number found the real gap: an unredirected `_place_air_intercept` ball
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(spawned 6-12m up, aimed at a goal whose collision box sits at ~0-1.5m) sags
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well short of the goal from gravity alone over the required flight
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distance — it does not auto-score — so it just falls to the floor, where the
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already-solved ground game collects the exact same `goal_reward`/
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`ball_touch_reward` regardless of whether anything touched it in the air.
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Nothing in the reward ever made a genuinely aerial touch worth more than
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waiting the second or two for the ball to land, so `air_approach_weight`'s
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dense closing-speed shaping had nothing to reinforce — a second,
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independent missing-incentive gap in the same stage, not a training-duration
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problem. `air_touch_bonus_weight` (`ship_ai_controller.gd`) closes it
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directly: an event bonus on top of `ball_touch_reward`, paid only for a
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touch that is both above `AIR_TOUCH_HEIGHT` and goal-directed, scaled by the
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exact same alignment factor already gating the base touch reward —
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conjunctive, not standalone, so it targets exactly the behaviour
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`productive_air_touch_fraction` measures without reopening the RLGym
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wall-bounce exploit the original "no standalone air-touch reward" decision
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(above) was written to avoid: air-intercept/air-drill spawns are kept away
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from every wall by construction. Set to 0.5 (roughly `ball_touch_reward`'s
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own magnitude) and folded into `HANDLING_REWARD_FLAGS`. The three blocked
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attempts were deleted and Stage 5 restarts from Stage 4's checkpoint again
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with both terms active.
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Stage 6's `league` opponent mode samples a historical exported policy at each
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episode reset. Each later stage preserves the preceding shaping and adds one
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new difficulty.
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@@ -181,10 +181,40 @@ STANDING_ARGS = ["--ent-coef", "0.01", "--entropy-floor"]
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# than continuing retry2's, same reasoning as every previous mechanism
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# change in this file: don't resume a policy shaped by an absent term into
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# one where it now exists.
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#
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# Round 8 (2026-08-19): air_approach_weight alone did not move the needle
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# either -- another full 180M-step chain (3 more attempts, 360M cumulative
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# across all six Stage-5 attempts) closed with productive_air_touch_fraction
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# still exactly 0.0 and air_touch_fraction at noise level, while goal_rate
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# kept passing its (lower) floor. Working out the physics instead of just
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# re-tuning a number found why: an unredirected air-intercept ball (spawned
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# 6-12m up, aimed at a goal whose collision box sits at ~0-1.5m) sags well
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# short of the goal from gravity alone over the required flight distance --
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# it does not auto-score -- so it simply falls to the floor, and the
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# already-solved ground game (forward_velocity_to_ball_weight, ball_touch_
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# reward, goal_reward) collects the exact same total episode reward either
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# way. Nothing ever made touching the ball while it was still genuinely
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# airborne worth more than waiting the second or two for it to land, so
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# air_approach_weight's dense closing-speed shaping had nothing to reinforce
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# -- nowhere near a training-duration problem, a second missing-incentive
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# gap in the same stage.
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#
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# air_touch_bonus_weight (ship_ai_controller.gd) closes it directly: an
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# event bonus on top of ball_touch_reward, paid only for a touch that is
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# both above AIR_TOUCH_HEIGHT and goal-directed, scaled by the exact same
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# alignment factor already gating the base touch reward -- conjunctive, not
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# standalone, so it can't be farmed by batting the ball in a useless
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# direction, and it targets exactly the behaviour productive_air_touch_
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# fraction measures instead of only the approach to it. Set to 0.5 (roughly
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# ball_touch_reward's own magnitude, so a fully-aligned aerial touch pays
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# ~1.7x a fully-aligned ground one). Also folded into HANDLING_REWARD_FLAGS
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# so Stage 6 inherits it. Restarts Stage 5 from Stage 4's checkpoint again,
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# same reasoning as every prior mechanism change here.
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HANDLING_REWARD_FLAGS = [
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"--velocity-to-ball-weight", "0.04",
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"--forward-velocity-to-ball-weight", "0.15",
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"--air-approach-weight", "0.15",
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"--air-touch-bonus-weight", "0.5",
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"--ball-distance-penalty", "0.01",
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"--ball-touch-reward", "0.7",
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"--ball-velocity-to-goal-weight", "0.06",
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@@ -1,7 +1,7 @@
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{
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"stage_index": 1,
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"attempt": 2,
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"status": "blocked",
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"attempt": 0,
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"status": "in_progress",
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"log": [
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{
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"stage_index": 0,
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@@ -258,254 +258,6 @@
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],
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"reason": "Human override. Only miss was the training goal-rate floor, at 0.7731 vs 0.80; every evaluation gate passed (65-22-13 vs promoted/easy.json, 87% non-draw, 12.6% physical-side imbalance) and the goal rate improved monotonically across all three attempts (0.537 -> 0.683 -> 0.773). Also promoted to Game/bots/promoted/medium.json on the same evidence. Stage 5 resumes from this checkpoint."
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}
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},
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{
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"stage_index": 1,
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"stage_number": 5,
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"stage_name": "intercepts",
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"experiment": "20260818-1903-gen5-s5-intercepts",
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"attempt": 0,
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"telemetry_tail": {
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"rollout/air_touch_fraction": 0.00011999999731779099,
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"rollout/airborne_fraction": 0.2547863810658455,
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"rollout/ep_len_mean": 97.30996008300781,
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"rollout/ep_rew_mean": 16.281804374694826,
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"rollout/forward_motion_fraction": 0.33076866614818573,
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"rollout/goal_rate": 0.8198399951457978,
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"rollout/grounded_upright_fraction": 0.1814400002658367,
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"rollout/mean_altitude": 3.249552144050598,
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"rollout/productive_air_touch_fraction": 0.0,
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"rollout/upright_fraction": 0.7921435704231262,
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"rollout/vertical_thrust_mean": 0.06410999808754772
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},
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"telemetry_failures": [
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"rollout/productive_air_touch_fraction=0.0000 < 0.0050"
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],
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"evaluation_goal_failures": [],
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"side_balance_failures": [],
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"eval": {
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"timestamp": "2026-08-19T03:12:42+00:00",
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"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260818-1903-gen5-s5-intercepts.json",
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"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
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"seed": 1,
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"episodes": 100,
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"wins_a": 55,
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"wins_b": 30,
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"draws": 15,
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"side_results": {
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"a_team_0": {
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"wins_a": 25,
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"wins_b": 17,
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"draws": 8
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},
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"a_team_1": {
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"wins_a": 30,
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"wins_b": 13,
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"draws": 7
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}
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},
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"physical_team_wins": {
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"team_0": 38,
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"team_1": 47
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},
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"win_rate_a": 0.55
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},
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"evals": [
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{
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"timestamp": "2026-08-19T03:12:42+00:00",
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"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260818-1903-gen5-s5-intercepts.json",
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"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
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"seed": 1,
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"episodes": 100,
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"wins_a": 55,
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"wins_b": 30,
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"draws": 15,
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"side_results": {
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"a_team_0": {
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"wins_a": 25,
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"wins_b": 17,
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"draws": 8
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},
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"a_team_1": {
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"wins_a": 30,
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"wins_b": 13,
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"draws": 7
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}
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},
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"physical_team_wins": {
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"team_0": 38,
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"team_1": 47
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},
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"win_rate_a": 0.55
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}
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],
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"decision": "fail"
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},
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{
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"stage_index": 1,
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"stage_number": 5,
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"stage_name": "intercepts",
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"experiment": "20260819-0412-gen5-s5-intercepts-retry1",
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"attempt": 1,
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"telemetry_tail": {
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"rollout/air_touch_fraction": 0.00011999999731779099,
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"rollout/airborne_fraction": 0.25744442877173424,
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"rollout/ep_len_mean": 112.18267991638183,
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"rollout/ep_rew_mean": 20.06156894302368,
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"rollout/forward_motion_fraction": 0.32834857150912283,
|
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"rollout/goal_rate": 0.7362400003671646,
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"rollout/grounded_upright_fraction": 0.17714000023156404,
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"rollout/mean_altitude": 3.2925854358673097,
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"rollout/productive_air_touch_fraction": 0.0,
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"rollout/upright_fraction": 0.7877685700654984,
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"rollout/vertical_thrust_mean": 0.09532099807504565
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},
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"telemetry_failures": [
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"rollout/goal_rate=0.7362 < 0.7500",
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"rollout/productive_air_touch_fraction=0.0000 < 0.0050"
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],
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"evaluation_goal_failures": [],
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"side_balance_failures": [],
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"eval": {
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"timestamp": "2026-08-19T12:21:42+00:00",
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"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260819-0412-gen5-s5-intercepts-retry1.json",
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"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
|
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"seed": 1,
|
||||
"episodes": 100,
|
||||
"wins_a": 58,
|
||||
"wins_b": 33,
|
||||
"draws": 9,
|
||||
"side_results": {
|
||||
"a_team_0": {
|
||||
"wins_a": 26,
|
||||
"wins_b": 22,
|
||||
"draws": 2
|
||||
},
|
||||
"a_team_1": {
|
||||
"wins_a": 32,
|
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"wins_b": 11,
|
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"draws": 7
|
||||
}
|
||||
},
|
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"physical_team_wins": {
|
||||
"team_0": 37,
|
||||
"team_1": 54
|
||||
},
|
||||
"win_rate_a": 0.58
|
||||
},
|
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"evals": [
|
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{
|
||||
"timestamp": "2026-08-19T12:21:42+00:00",
|
||||
"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260819-0412-gen5-s5-intercepts-retry1.json",
|
||||
"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
|
||||
"seed": 1,
|
||||
"episodes": 100,
|
||||
"wins_a": 58,
|
||||
"wins_b": 33,
|
||||
"draws": 9,
|
||||
"side_results": {
|
||||
"a_team_0": {
|
||||
"wins_a": 26,
|
||||
"wins_b": 22,
|
||||
"draws": 2
|
||||
},
|
||||
"a_team_1": {
|
||||
"wins_a": 32,
|
||||
"wins_b": 11,
|
||||
"draws": 7
|
||||
}
|
||||
},
|
||||
"physical_team_wins": {
|
||||
"team_0": 37,
|
||||
"team_1": 54
|
||||
},
|
||||
"win_rate_a": 0.58
|
||||
}
|
||||
],
|
||||
"decision": "fail"
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||||
},
|
||||
{
|
||||
"stage_index": 1,
|
||||
"stage_number": 5,
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"stage_name": "intercepts",
|
||||
"experiment": "20260819-1321-gen5-s5-intercepts-retry2",
|
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"attempt": 2,
|
||||
"telemetry_tail": {
|
||||
"rollout/air_touch_fraction": 0.0,
|
||||
"rollout/airborne_fraction": 0.2547037144303322,
|
||||
"rollout/ep_len_mean": 108.22539994812011,
|
||||
"rollout/ep_rew_mean": 20.78616273498535,
|
||||
"rollout/forward_motion_fraction": 0.33834266650676725,
|
||||
"rollout/goal_rate": 0.7449199994802475,
|
||||
"rollout/grounded_upright_fraction": 0.22299999982118607,
|
||||
"rollout/mean_altitude": 3.228344327926636,
|
||||
"rollout/productive_air_touch_fraction": 0.0,
|
||||
"rollout/upright_fraction": 0.7867764747142791,
|
||||
"rollout/vertical_thrust_mean": 0.03713899875985459
|
||||
},
|
||||
"telemetry_failures": [
|
||||
"rollout/goal_rate=0.7449 < 0.7500",
|
||||
"rollout/productive_air_touch_fraction=0.0000 < 0.0050"
|
||||
],
|
||||
"evaluation_goal_failures": [],
|
||||
"side_balance_failures": [],
|
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"eval": {
|
||||
"timestamp": "2026-08-19T21:34:06+00:00",
|
||||
"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260819-1321-gen5-s5-intercepts-retry2.json",
|
||||
"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
|
||||
"seed": 1,
|
||||
"episodes": 100,
|
||||
"wins_a": 56,
|
||||
"wins_b": 31,
|
||||
"draws": 13,
|
||||
"side_results": {
|
||||
"a_team_0": {
|
||||
"wins_a": 25,
|
||||
"wins_b": 16,
|
||||
"draws": 9
|
||||
},
|
||||
"a_team_1": {
|
||||
"wins_a": 31,
|
||||
"wins_b": 15,
|
||||
"draws": 4
|
||||
}
|
||||
},
|
||||
"physical_team_wins": {
|
||||
"team_0": 40,
|
||||
"team_1": 47
|
||||
},
|
||||
"win_rate_a": 0.56
|
||||
},
|
||||
"evals": [
|
||||
{
|
||||
"timestamp": "2026-08-19T21:34:06+00:00",
|
||||
"model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260819-1321-gen5-s5-intercepts-retry2.json",
|
||||
"model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
|
||||
"seed": 1,
|
||||
"episodes": 100,
|
||||
"wins_a": 56,
|
||||
"wins_b": 31,
|
||||
"draws": 13,
|
||||
"side_results": {
|
||||
"a_team_0": {
|
||||
"wins_a": 25,
|
||||
"wins_b": 16,
|
||||
"draws": 9
|
||||
},
|
||||
"a_team_1": {
|
||||
"wins_a": 31,
|
||||
"wins_b": 15,
|
||||
"draws": 4
|
||||
}
|
||||
},
|
||||
"physical_team_wins": {
|
||||
"team_0": 40,
|
||||
"team_1": 47
|
||||
},
|
||||
"win_rate_a": 0.56
|
||||
}
|
||||
],
|
||||
"decision": "fail"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
BIN
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BIN
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@@ -360,6 +360,12 @@ def parse_args():
|
||||
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(
|
||||
"--air-touch-bonus-weight", type=float, default=None,
|
||||
help="Event bonus on top of ball-touch-reward for a touch that is both genuinely aerial "
|
||||
"(ball above AIR_TOUCH_HEIGHT) and goal-directed, scaled by the same alignment factor "
|
||||
"as the base touch reward",
|
||||
)
|
||||
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)",
|
||||
@@ -425,6 +431,7 @@ def _curriculum_kwargs(args) -> dict:
|
||||
"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_air_touch_bonus_weight": args.air_touch_bonus_weight,
|
||||
"ai_ball_distance_penalty": args.ball_distance_penalty,
|
||||
"ai_ball_touch_reward": args.ball_touch_reward,
|
||||
"ai_airborne_penalty": args.airborne_penalty,
|
||||
|
||||
Reference in New Issue
Block a user