Model persistence round-trip for ActorCriticNetworks

[belarusian]

TICKET-052: Model persistence round-trip for ActorCriticNetworks

Status: OPEN
Cycle: 43
Priority: High
Target module: alloc/models/networks.py — ActorCriticNetworks

Summary

Backtest mode (alloc/core.py main) saves actor_weights.h5 and
critic_weights.h5 after training, but there is no load path: a saved
model cannot be re-instantiated. The seed's predict mode loads a previously
trained model before producing a live allocation. To close that parity gap
(see TICKET-053), ActorCriticNetworks needs a save/load round-trip that
persists both the weights and the config needed to reconstruct the
network (input_dim, num_assets, min_cash_allocation, and the hyperparameters
that affect architecture: dropout, gamma, tau).

Evidence

• alloc/core.py lines 733-735: networks.actor.save_weights(...) /
  networks.critic.save_weights(...) — save only, never load.
• alloc/models/networks.py ActorCriticNetworks.__init__ (lines 285-340)
  builds actor/critic/targets/optimizers from input_dim, num_assets,
  min_cash_allocation, dropout. There is no save_model/load_model
  method and no config file is written.
• The actor architecture depends on input_dim and num_assets (per-asset
  branch widths scale with the asset index), so loading weights into a
  freshly-built network with the wrong dimensions will fail or silently
  mismatch. The config must be persisted alongside the weights.

Implementation plan

1. ActorCriticNetworks.save_model(directory) — write:
   • actor_weights.h5, critic_weights.h5 (via keras save_weights).
   • model_config.json with input_dim, num_assets,
     min_cash_allocation, dropout, gamma, tau, actor_lr,
     critic_lr.
2. ActorCriticNetworks.load_model(directory) (classmethod) — read
   model_config.json, construct an ActorCriticNetworks with those
   parameters, then actor.load_weights / critic.load_weights, and
   re-sync the target networks (actor_target.set_weights,
   critic_target.set_weights). Return the instance.
3. Raise FileNotFoundError with a clear message if model_config.json or
   either weights file is missing.

Verification

• pytest tests/test_actor_critic.py -x -q — new round-trip test passes:
  build a small network, save to a tmp dir, load, assert
  get_allocation(state) is identical (or near-identical) before/after and
  that config fields round-trip.
• ruff check alloc/models/networks.py — clean.
• mypy alloc/models/networks.py --ignore-missing-imports — clean.