All checkpoints/logs/exported policies here are a continuous-Gaussian,
31-input action/observation shape that generation 4's MultiDiscrete
redesign is structurally incompatible with -- nothing to resume from (see
the prior commit and TRAINING.md's "Generation 4" section). Game/bots/promoted/
(easy.json, and the new reference-grounded.json copied from
curric-s5-aggression before this) is untouched -- both remain valid,
playable evaluation opponents forever via PolicyNetwork's format-versioned
JSON despite their own checkpoints/generation being gone.
- training/checkpoints/*, training/logs/* removed (~3.5GB of working tree,
all generation 1-3 experiment runs).
- Game/bots/*.json flat dump removed (superseded exports; main_menu.gd's
Spectate dropdown will just be empty until the first generation-4 export).
- curriculum_state.json -> curriculum_state_gen3.json, archived alongside
the existing _gen1/_gen2 logs (all three are referenced as postmortem
evidence in TRAINING.md/curriculum.py). A fresh curriculum_state.json
will be created on the next curriculum.py run (load_state() already
handles a missing file).
training/eval_history.json is deliberately NOT reset -- it's the one
continuous cross-generation progress record.
NOT YET PUSHED: this needs the remote Linux training box quiesced first
(kill any active tmux session, confirm it's synced to origin) so its own
run_training.sh doesn't race a still-running job's final commit against
this deletion.
Generation 2's first two real stage-1 attempts both independently restarted
from curric-s5-aggression (reset_retry_checkpoint) with identical flags and
landed at 32% and 27% win rate vs the reference -- a real regression either
way, but too much spread between "identical" runs for repeat fresh restarts
to be a controlled test of anything. The first attempt's own trajectory
(ep_rew_mean climbing from -10.86 toward ~0 by the 240M-step cutoff,
briefly touching positive) looked closer to convergence than the second's,
so retries now continue that attempt's own checkpoint for another full
timesteps budget instead of resetting to foundation again.
Drops retry1 and retry2 (checkpoints, logs, exported bots, eval_history
entries) -- retry2 never trained meaningfully before crashing on the
GoalRateCallback bug just fixed, and retry1 was the inferior of the two
real samples. curriculum_state.json rewinds to attempt 1, in_progress, so
the next run resumes 20260726-1904-curric-s1-unmask/final.zip directly.
CosmicClash Training Bot