Introduce arena_02 (Nebula) and arena_03 (Asteroid Field) alongside
arena_01, listed in a new ArenaRegistry (scripts/arena_registry.gd) as
the single source of truth for available arenas. Free Play lets the
player pick an arena from the main menu; Match/Spectate each pick one
at random per session; Training keeps its own fixed arena_01.
Nebula gets an RL-style visual treatment: a near-invisible glass
boundary material shared by all arenas, a baked equirect nebula sky
texture, a drifting-dust particle system, and a Blender-modeled
station/debris/planet decoration set. GameMode gains a
_get_arena_scene_path() hook so modes can instantiate their arena in
code instead of hardcoding it in the scene.
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.
Josh Creek