Diagnosis: mcts/value_prediction_error stuck at 300-1000 (RMSE ~22 on score
range ±100), while loss/value stays at 0.05 because the loss divides
prediction and target by value_scale=100 (so MSE / 10000). Net effect: the
value head receives a tiny gradient relative to the policy head's
~1.75 cross-entropy loss, so it never learns to predict score scale well.
This adds a config knob to multiply the normalized value loss without
re-engineering the loss formula. value_loss_weight=50 recovers the raw
MSE magnitude (~2.5 vs policy loss ~1.75), giving the value head
comparable gradient signal.
Trap diagnosis: agent learned to stall (avoid opening expeditions, draw
from discard pile to extend deck) until max_steps timeout, then squeak by
on opponents' negative scores. All eval wins were from timeouts; agent
never won a naturally-terminating game. Self-play reinforced this because
timeout games still got a positive value target.
Fixes (no algorithm change, all MCTS hyperparameters or signal shaping):
- Dirichlet noise at root prior (AlphaZero standard, was missing):
mcts.pyx `_expand_with_prior` takes `is_root` flag; root expansion
mixes prior with Dirichlet(α). Callers in interleaved_self_play and
the internal evaluate_and_backup pass `not item.path`.
- Default config strengthens exploration on the 50-sim batched search:
c_puct 1.5 -> 3.0, virtual_loss_value 1.0 -> 5.0, plus new
root_dirichlet_alpha=0.3 / root_dirichlet_epsilon=0.25.
- Self-play timeout signal zeroed: `_finalize_context` sets v_target=0
if context.state is not terminal. Stops the network from learning
"stall = positive value".
New standalone evaluator:
- `lost-cities-ismcts eval` subcommand (eval_checkpoint.py): loads a
checkpoint, runs N games per opponent across a parallel pool, reports
win/score with 95% CIs plus per-game logging via --verbose. Defaults
cover heuristic-balanced/aggressive/cautious (rollout policy isn't in
the training-eval opponent list, so this is the natural way to compare
the trained policy against its rollout target).
Tests (19) still pass; .so rebuilt.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Key changes for ISMCTS speed and correctness:
- Cython port: HeuristicBot helpers (`heuristic_cy.pyx` + new `.pxd`) and
ISMCTS searcher (`mcts.pyx`) now run as cdef. Both share a fast
unified-action path through GameState's C interface to avoid Python
round-trips on hot rollout/tree-walk paths.
- Multi-process self-play and eval: `workers.py`, `eval_worker.py`,
`interleaved_self_play.py`, plus trainer wiring with ProcessPoolExecutor
+ spawn context. Eval inside `evaluate.py` is parallel per opponent.
- ISMCTS-specific eval (`evaluate.py`) runs MCTS at decision time so the
metric matches deploy mode; `evaluation.eval_with_mcts` flag preserves
backwards-compatible policy-only eval when needed.
- Trainer logs progress per phase (self-play start/done, eval per
opponent), and value loss is now scaled by `value_scale` so policy and
value losses sit on comparable magnitudes.
- Compact info-set key (`info_set.py`) using packed-struct format and
child-key reuse during MCTS descent to cut per-step canonicalization.
Tests: 19 ISMCTS suite passing, including parity (Cython-vs-Python
sequential, batched-vs-sequential visit counts, push/pop round-trip).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Implements a proof-of-concept single-observer IS-MCTS trainer with AlphaZero-style policy/value network, determinization, replay, self-play, CLI configs, and focused tests. Mini acceptance run reaches positive random eval while keeping play_action_rate above the Deep CFR trap threshold.
Tests: uv run python -m pytest tests/games/classic/ismcts/ -x; uv run python -m pytest tests/games/classic/test_deep_cfr_trainer.py -x; uv run lost-cities-ismcts train --config configs/ismcts/mini.yaml
coolguy
