6.1: "Linux Dedicated Server" preset (dedicated_server=true,
custom_features="dedicated_server") mirroring the existing training
preset, plus run/main_scene.dedicated_server so the server binary reaches
its own entry point with no flag. Builds: an 85MB Linux x86_64 binary,
gitignored like the training one.
6.3: scripts/server_config.gd declares every server flag once - name,
type, default, section, help - and one parser turns that into parsing,
type checking, range validation, config-file backing and --help. The
flags had grown to ~30 across server_boot.gd and networked_match.gd, each
parsed inline with begins_with, none documented, and an unrecognised flag
was SILENTLY IGNORED: --max-clientss=8 ran a server on the default cap
and said nothing. Unknown flags, missing values, wrong types, duplicates
and out-of-range values are now hard errors, reported all at once.
Precedence is command line > config file > default. server_boot.gd parses
strictly because it owns the whole command line; networked_match.gd reads
the same declaration leniently because it is one consumer of an argv the
smoke harnesses also fill with --role= and --drive-seconds=. Nothing is
lost - every server flag is declared, so the strict pass already caught
any typo before the match scene re-reads its own.
13 unit tests covering the precedence order, the typo rejection that
motivated this, --no-<bool> not double-listing in --help, and --help
documenting every flag asserted against the declaration rather than a
hand-kept list. Verified end to end: --help prints, a typo'd flag refuses
to start, and the plain/replay-log/late-joiner smoke scenarios still pass.
Three curriculum generations (2026-07-21 through 2026-08-04) all tried
gating *when* the policy could use vertical thrust/pitch-roll on top of a
continuous Gaussian action space, and all three failed the same way: PPO's
action-distribution std collapsed within ~10% of steps and never recovered,
landing at a 15-32% win rate vs the grounded reference regardless of
mechanism (hard mask, then a gradual ramp). Generation 3's final attempt
just landed at 24% — the worst of the three.
Root cause, verified against this project's own physics: hovering this ship
requires *holding* thrust.y ~= 0.408 continuously (mass 5.0, vertical_thrust
120, gravity 9.8). A collapsed near-zero-mean Gaussian can brush that value
but never sustain it long enough to earn the reward gradient that would
move the mean — no amount of gating *when* the axis acts fixes a problem in
*how* the policy represents a decision on it. This also independently found
and fixes a real bug: godot_rl never marks an episode timeout as a
truncation, so PPO was bootstrapping V(s)=0 on every 30s draw in every
generation to date.
- Game/scripts/ship_action_codec.gd (new): single source of truth for a
per-axis MultiDiscrete action space (7 heads, nvec [5,5,5,5,5,5,2]) shared
by training and in-game inference, replacing the continuous Gaussian.
thrust_y's bins are deliberately asymmetric so a random policy drifts
through the volume instead of floor-pinning. Legacy continuous decode
(ai_ship_controller.gd's old logic) preserved verbatim so every
pre-generation-4 export (e.g. Game/bots/promoted/easy.json) keeps working
unchanged via an optional "action_space" JSON field.
- ship_observations.gd: append own contact state (SIZE 31 -> 35, append-only)
so the value function can see what wall_contact_penalty fires on.
- ship_ai_controller.gd: action space/decode via the codec; drop the
vertical_ramp/pitch_roll_ramp mechanism entirely; tilt_penalty default
lowered 4x (aerial approaches require pitching); flight telemetry
(airborne_fraction, mean_altitude, air_touch_fraction, vertical_thrust_mean)
and truncation-snapshot fields on get_info().
- training_mode.gd: new air_drill_chance state-setter branch (ball spawned
high, ships low, kept clear of walls) so aerial practice is forced by the
environment instead of relying on reward-driven exploration alone; snapshot
terminal observations before a timeout reset for the truncation fix.
- cosmic_env.py: remap ShipAIController's truncated/terminal_obs info into
SB3's TimeLimit.truncated/terminal_observation keys.
- train.py: --reset-logits (+ --reset-logits-heads) replaces the
now-meaningless --reset-std; new EntropyFloorCallback (a persistent
per-rollout ent_coef controller replacing the one-shot std-reset shock)
and per-head entropy logging; FlightTelemetryCallback; --air-drill-chance/
--tilt-penalty flags; optional AbortIfCallback kill-criterion.
- export_policy.py: writes the action_space block for MultiDiscrete models;
index-level parity check (argmax per head) instead of comparing floats.
- curriculum.py: full rewrite — 3 stages (bootstrap/selfplay/gauntlet), no
grounded stage, full action space live from step 1; deletes generation
1-3's checkpoint-lineage machinery (nothing to resume from); final report
evaluates against both promoted/easy.json and the new
promoted/reference-grounded.json (a copy of curric-s5-aggression, the
strongest grounded-era artifact, kept as a fixed yardstick).
- run_training.sh/.gitignore: commit only final.zip, not the ~2400
intermediate checkpoint files a single stage was writing (~500MB ->
~0.2MB per run); requirements.txt pinned (behaviour here now depends on
specific library internals, not just public APIs).
- test_action_space.py (new): offline rung-0 check catching a head-order
mismatch before it silently corrupts 24h of training.
Validated: GDScript compiles clean (Godot --headless --import + script
validation), free_play.tscn and training.tscn both boot headless without
errors, offline action-space assertions pass. Not yet run: the actual
smoke-training/A-B validation ladder steps in TRAINING.md's "Generation 4"
section, before committing to the full ~32h curriculum.
See TRAINING.md's "Generation 4" section for the full design writeup.
Every bright star in sky_nebula.png reused the exact same diffraction-
spike stamp, just relocated. Commit the generator (recovered from an
ephemeral scratchpad) to tools/textures/gen_nebula_sky.py, randomize
each hero star's rotation, arm count, spike length, and brightness,
and regenerate the texture.
Josh Creek