Promote avg-strategy 1000iter to default.yaml; archive other configs

The avg-strategy 1000iter file (with the recent +traversals/+LR/+LCFR
changes) is the canonical "best-known" config. Renamed it to
default.yaml so users start from a single, obvious entry point and
override one field per ablation via --set. Other 12 configs moved to
configs/archive/ — kept for historical reproduction, not for active use.

- configs/deep_cfr/{default.yaml, smoke.yaml} are the only active configs
- experiment_name shortened to "deep-cfr-default" (was a long mouthful)
- AGENTS.md examples and Project Layout section rewritten around
  default.yaml; ablation example shows the override-one-field pattern
- Tests pointed at the archived slot-playability config for the legacy
  reproduction assertions

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

configs/archive/deep-cfr-opponent-network-512x3-1000iter.yaml

@@ -0,0 +1,104 @@
+run:
+  experiment_name: lost-cities-deep-cfr-opponent-network-512x3-1000iter
+  seed: 79
+  max_iterations: 1000
+  max_minutes: null
+  device: cuda
+  use_amp: false
+
+rules:
+  n_colors: 5
+  n_ranks: 9
+  min_rank: 2
+  n_handshakes: 3
+  hand_size: 8
+  expedition_penalty: -20
+  bonus_threshold: 8
+  bonus_amount: 20
+
+encoding:
+  derived_playability: true
+  slot_aware_playability: true
+
+network:
+  hidden_size: 512
+  num_layers: 3
+  activation: relu
+
+traversal:
+  traversals_per_player: 2
+  traversals_per_player: 70
+  max_depth: null
+  max_nodes_per_traversal: 10000
+  max_nodes_per_traversal: 1000
+  regret_matching_epsilon: 0.0001
+  outcome_sampling_epsilon: 0.2
+  outcome_sampling_value_clip: 500.0
+  outcome_unsampled_regret: zero
+  cutoff_value_mode: score_diff
+  cutoff_rollouts: 0
+  cutoff_rollout_policy: random
+  cutoff_rollout_max_steps: 300
+  opponent_policy: network
+  strategy_sample_interval: 1
+  store_strategy_on_traverser_nodes: true
+  store_strategy_on_opponent_nodes: false
+  num_workers: 8
+  worker_chunk_size: 4
+  worker_chunk_size: 8
+  progress_every_traversals: 10
+  endpoint_depth_bucket_width: 100
+  endpoint_depth_bucket_max: 1000
+
+regret_matching:
+  all_negative_fallback: argmax_tiebreak
+
+training_weighting:
+  mode: none
+
+self_play:
+  snapshot_every: 1
+  max_snapshots: 0
+  anchor_probability: 0.0
+  current_weight: 1.0
+  recent_weight: 0.0
+  older_weight: 0.0
+  anchor_weight: 0.0
+  recent_window: 5
+
+optimization:
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
+  advantage_batch_size: 1024
+  strategy_batch_size: 1024
+  advantage_updates_per_iteration: 512
+  strategy_updates_per_iteration: 512
+  learning_rate: 0.00003
+  weight_decay: 0.0001
+  grad_clip: 1.0
+
+memory:
+  advantage_capacity: 2000000
+  strategy_capacity: 2000000
+
+checkpoint:
+  save_latest: true
+  save_every: 100
+  progress_interval_seconds: 20.0
+  exact_resume: false
+
+evaluation:
+  eval_every: 5
+  games: 100
+  opponents:
+    - random
+    - passive_discard
+    - safe_heuristic
+    - safe_heuristic_loose
+    - safe_heuristic_strict
+    - noisy_safe
+  max_steps: 10000
+  on_max_steps: score_diff
+  batch_size: 64
+  device: trainer
+  num_workers: 4