feat(training): add generation 5 curriculum

TODO.md

@@ -6,9 +6,11 @@ Deferred work, in rough priority order. The current architecture (ShipAction/Shi
 
 The training pipeline is built — see `TRAINING.md` (self-play PPO via the vendored godot_rl_agents bridge, JSON policy export, in-game GDScript inference, eval ladder). Remaining:
 
-- [ ] Long training runs on the Linux/3090 box to produce actually-good bots; promote further checkpoints into `Game/bots/promoted/` as `medium`/`hard` tiers once they clear `easy.json` in `evaluate.py`.
-- [ ] Frozen-opponent league: train the live policy against a *pool* of past exported checkpoints, sampled per-episode (today's `--opponent-mode=frozen` only supports one fixed model per run) to prevent self-play strategy collapse on long runs.
-- [ ] Richer state setter / curriculum: aerial states, wall plays, rebound scenarios as skill grows (beyond the score/defend/draw staging already in place).
+- [x] Promote generation 4's stage-3 gauntlet policy as the new `Game/bots/promoted/easy.json` baseline.
+- [ ] Run the generation-5 handling/intercepts/league/teamplay curriculum described in `TRAINING.md`; promote later checkpoints as `medium`/`hard` only after they clear the match and behaviour gates.
+- [x] Frozen-opponent league plumbing: `--opponent-mode=league` samples a past exported checkpoint per episode; generation 5 Stage 6 supplies the curated pool.
+- [ ] Extend generation 5's moving aerial-intercept states with wall plays and rebound scenarios after Stage 5 establishes a productive-air-touch baseline.
+- [ ] Design team-credit rewards and paired 2v2 evaluation before enabling the deferred teamplay stage.
 
 ## Presentation / AAA polish