Replace the raw checkpoint dropdown with curated Easy/Medium/Hard presets
that drive GameSettings' bot model/reaction_ticks/action_noise overrides.
Move raw-checkpoint testing and Spectate mode into a dev-only section
hidden via OS.is_debug_build() so they disappear from release exports.
Generation 2's single "unmask" stage (flip vertical/pitch-roll locomotion
from grounded-only to full 3D in one step) failed 3 independent 240M-step
attempts, landing at a stable 32% / 28% / 31% win rate vs curric-s5-aggression
each time -- not noise, and not fixable by more training time (attempts 2-3
each continued the same checkpoint lineage for another full 240M steps with
zero improvement). Every attempt shows train/std collapsing from ~0.30 to
~0.13-0.15 within the first ~10% of steps and never recovering: the policy
locks the newly-opened axes back down before ever meaningfully exploring
them.
Replaces the boolean allow_vertical/allow_pitch_roll mask on ShipAIController
with float vertical_ramp/pitch_roll_ramp multipliers (0.0-1.0), scaling axis
effect in set_action() instead of gating it outright -- the action space
never changes shape, so checkpoints stay resumable across ramp values. The
single unmask stage in curriculum.py becomes 4: three ungated warmup stages
(25%/50%/75% authority, airborne_penalty ramping in step) that train,
checkpoint, and always advance with no eval gate, then the measured stage at
full authority -- same reference, opponent mode, and 240M budget as the 3
failed attempts, for a direct comparison. Adds a "gated" flag/branch to
main()'s loop for the ungated stages.
This is generation 3 of the curriculum; generation 2's state is archived to
curriculum_state_gen2.json (mirroring the earlier gen1 -> gen2 archival) and
curriculum_state.json resets fresh, since its stage 0 no longer means what it
used to. See TRAINING.md's "Generation 3" section for the full postmortem,
stage table, and the open question about whether scaling action effect in
Godot (which PPO's own entropy/exploration math never sees) actually
addresses the collapse.
Stage 5 (aggression) passed (41-47 vs grounded curric-s2-defend, within
the lenient gate but not yet a clear win). Rather than keep the locomotion
mask on indefinitely, stage 6 reopens full 3D controls on top of the
aggression retune and pairs it with a new dense airborne_penalty (scaled
by height above the floor) so the policy learns to prefer staying grounded
through incentives instead of a hard mask — same regime shift that
regressed stage 3, but this time with a mitigation and ~12x the training
time (~240M timesteps / ~24h vs ~20M / ~2h) to actually re-converge
instead of stalling mid-shift.
airborne_penalty follows the existing SHIP_AI_OVERRIDES pattern: default
0 (off) on ship_ai_controller.gd, exposed via train.py's new
--airborne-penalty flag, added to training_mode.gd's allow-list. Also adds
a per-stage timesteps override in curriculum.py (STAGES[n]["timesteps"])
since this is the first stage to need a different budget than the rest.
GameMode.reset_ball()/reset_ships() teleported to exact, identical spawn
transforms every kickoff. Combined with deterministic bot inference
(action_noise = 0 by default), two ships running the same policy from a
mirror-symmetric state produced mirrored, non-diverging play instead of a
real contest — most visible when both sides use the same exported model.
Adds a small position/yaw jitter (well under anything a player would
notice as "not a real kickoff") so kickoff-style resets stop being
bit-for-bit identical.
AIShipController (eval + real gameplay) ran the raw policy output unmasked
regardless of allow_vertical/allow_pitch_roll, while ShipAIController
(training) correctly discarded those axes for grounded curriculum stages.
A grounded-trained model's untrained vertical/pitch-roll output reached the
ship as noise during eval, understating it against models that were never
handicapped this way.
Josh Creek