Adversarial review of the previous stage-4 retune found two problems:
non_forward_speed used planar_speed - forward_component, which under-charges
diagonal motion relative to true lateral speed (e.g. ~29% penalty at 45
degrees off the nose instead of the correct ~71%); fixed to the Pythagorean
magnitude for forward-facing angles, full speed for backward-facing ones.
Also, ground_tilt_penalty and non_forward_penalty only ever cost reward near
the floor with nothing offsetting them above it, which could teach a policy
that's still bad at ground handling to just avoid the floor rather than get
better at it. Added grounded_upright_reward (ship_ai_controller.gd) plus a
new ShipObservations.is_floor_contact helper for genuine belly-on-floor
contact detection, so grounding well while upright is the locally profitable
choice, not just the least-punished one.
Extends ShipObservations beyond the old self+1-opponent layout to padded
teammate/opponent arrays (MAX_TEAMMATES=4, MAX_OPPONENTS=5, SIZE=83),
zero-filling slots past the real roster size the same way the old single-
opponent slot was zero-filled when absent.
Slot stability across ticks requires a persistent identity: Ship gains
spawn_index (set once by GameMode.spawn_ship, never reassigned — there's no
despawn path anywhere in this codebase, so a roster is fixed for the whole
episode/match). ai_ship_controller.gd's opponent discovery is rewritten from
"first non-self ship" to classify every other ship by team and sort by
spawn_index; training_mode.gd/ship_ai_controller.gd carry the equivalent
sorted lists through the training path so both agree on slot assignment for
the same roster.
training_mode.gd and match_mode.gd both gain a team_size export (default 1,
so every existing curriculum script and match keeps today's 1v1 behaviour
unchanged). This is plumbing only: no 2v2+ curriculum or reward design, and
no match-mode UI to pick team size, has been done yet. The two checkpoints
in Game/bots/promoted/ are fitted to the old 35-float layout and are not
migrated — expected to go stale until the next training run.
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.
Josh Creek