Update paper models, inference code and model card

README.md

@@ -1,5 +1,7 @@
 ---
 license: cc-by-4.0
+language:
+  - en
 tags:
   - reinforcement-learning
   - multi-objective
@@ -7,93 +9,158 @@ tags:
   - attention
   - ppo
   - morl
-language:
-  - en
 pipeline_tag: reinforcement-learning
 ---
 
 # SmartFlexPack MORL: Sequential Attention Policy for Packaging Scheduling
 
-Trained model weights for the paper:
-
-> **Multi-Objective Reinforcement Learning for Adaptive Packaging Scheduling via Sequential Attention Policy**  
-> L. Venturoso, G. Egger, F. Visentin, M. Todescato  
-> Fraunhofer Italia Research EFRE1054 Smartflexpack  
-> *Submitted to IEEE Transactions on Industrial Informatics*
-
-## Model description
+Five trained PyTorch policies accompanying the manuscript *Multi-Objective
+Reinforcement Learning for Adaptive Packaging Scheduling via Sequential Attention
+Policy*, by L. Venturoso, G. Egger, F. Visentin and M. Todescato (2026,
+manuscript in preparation). Developed within the SMARTFLEXPACK project at
+Fraunhofer Italia.
 
-`SeqAttentionActorCritic`: a two-stage network (11,523 parameters) that encodes
-600 customer orders via 1-layer multi-head self-attention (4 heads, d=32) and
-decodes per-order binary mode decisions (inline vs. label) conditioned on a
-Dirichlet-sampled preference vector.
+## Model and task
 
-A single trained policy navigates the full cost–tardiness Pareto front at
-inference time with no retraining required when operational priorities shift.
+The policy chooses INLINE printing or LABEL packaging for each order when both
+modes are feasible. A preference vector `(tardiness, cost, CO2)`, with nonnegative
+entries summing to one, controls the trade-off at inference time.
+The planner handles order sequencing, forced modes including NO_PRINT, line
+assignment, timing and label reservations; the discrete-event simulation executes
+the resulting production plan.
 
-## Results
+`SeqAttentionActorCritic` has **12,355 parameters**:
 
-+23.7% Hypervolume over the factory heuristic (Wilcoxon W=173, p=0.0047,
-Cohen's d=0.404, n=20 order seeds). Highest Hypervolume across all 20
-evaluation seeds against four baselines (Heuristic, ATC, CR, CP-SAT).
+- Once per planning event: five features per pending order are embedded into
+  32 dimensions, with sinusoidal due-date-rank encoding and one self-attention
+  layer (four heads, residual connection and LayerNorm).
+- Once per decision: the 32-dimensional embedding is concatenated with ten live
+  decision features and twelve global features, including the three preferences.
+  A `54 → 64 → 64` tanh MLP feeds two action logits and a scalar critic.
 
-## Repository structure
+The encoder processes the orders pending at that event. The 600 orders in a
+training episode arrive across multiple planning events.
 
-```
-ppo/
-  seed_{1..5}/policy_200.pt   # PPO final checkpoints (5 independent seeds)
-  finalize.log                # Evaluation statistics used in the paper
-reinforce/
-  seed_{1..5}/policy_200.pt   # REINFORCE ablation (5 seeds)
-  finalize.log
-```
+## Training
 
-## Inference
+Behavioural cloning provides the warm start (240 demonstration episodes,
+40 epochs), followed by five independent PPO runs, each with 200 updates,
+ten parallel episodes per update and 600 orders per episode. Adam uses a learning
+rate of `3e-4`, clipping `0.2`, three PPO epochs, entropy coefficient `0.01` and
+value-loss coefficient `0.5`. The critic target is the final reward of the
+individual order, and advantages are batch-standardised.
+
+Preferences remain fixed within an episode. Half are sampled from
+Dirichlet(1,1,1); half favour a randomly selected objective with weight sampled
+uniformly from 0.6 to 0.9. Training varies the label-line count from one to five,
+with one inline line at 0.35 s/pack and a consolidating label-line rule.
+
+Rewards combine clipped, normalised tardiness, cost and emissions penalties,
+plus a 0.5 penalty for late DDP/DAP orders. Each order's cost includes an equal
+share of the episode's line operating cost. Reward normalisers are provided
+in `normalisation/reward_normalisers_consol.json`; observation transforms are
+implemented in the full project's `experiments/seq_morl/envs/observations.py`.
+
+These are the final historical paper checkpoints, with identical tensors to
+update 200. The supplied configuration and inference source reflect the current
+code. Current code also seeds BC initialization and orders parallel collection;
+retraining can differ from the historical runs. These weight files contain no
+optimizer or RNG state and support inference or fine-tuning rather than exact
+training continuation.
+
+## Evaluation
+
+Greedy evaluation uses 15 preference vectors and independent order scenarios.
+The primary configuration has three label lines and an inline processing time
+of 0.45 s/pack. Relative to the capacity-consolidating reference set C
+(32 controllers), the policy improves three-objective hypervolume by **+0.058**
+(95% crossed-bootstrap CI **[+0.025, +0.095]**, approximately **7%**).
+An additional independent test set confirms an advantage of +0.050
+(95% CI [+0.015, +0.084]). These intervals account for training-model and
+order-scenario variability.
+
+The advantage increases with a slower inline line (0.60 s/pack). Results with
+one label line are inconclusive; at the fastest inline setting (0.35 s/pack),
+one test shows no significant difference and another shows a small disadvantage.
+The paper also compares against an extended, 43-controller family.
+
+## Files
+
+| Path | Contents |
+|---|---|
+| `ppo/seed_{1..5}/policy_200.pt` | Five final PPO state dictionaries |
+| `ppo/seed_{1..5}/metrics.csv` | Historical training metrics |
+| `bc/bc_seq_morl.pt` | Shared BC warm-start weights |
+| `inference/seq_morl/` | Minimal PyTorch architecture and configuration source |
+| `configs/paper.json` | Current paper reproduction settings |
+| `normalisation/` | Reward normalisers and observation-scale measurements |
+| `manifest.json` | Source revision, checkpoint origins and SHA-256 file hashes |
+
+The earlier PPO weights and REINFORCE ablation remain available in repository
+history; this revision contains the current paper models.
+
+## Loading a policy
+
+Install `torch` and `huggingface_hub`. The following checks loading and tensor
+dimensions using **synthetic inputs**; real decisions require the full project's
+normalised observations in the same feature order.
 
 ```python
+import sys
+from pathlib import Path
 import torch
-from experiments.seq_morl.models import SeqAttentionActorCritic
+from huggingface_hub import snapshot_download
+
+release = Path(snapshot_download("fhi/smartflexpack-morl"))
+sys.path.insert(0, str(release / "inference"))
+from seq_morl.policies.attention import SeqAttentionActorCritic
 
 model = SeqAttentionActorCritic()
-model.load_state_dict(torch.load("ppo/seed_1/policy_200.pt", map_location="cpu"))
+weights = torch.load(release / "ppo/seed_1/policy_200.pt",
+                     map_location="cpu", weights_only=True)
+model.load_state_dict(weights, strict=True)
 model.eval()
 
-# encode order sequence (no-grad)
 with torch.no_grad():
-    ctx = model.encode_sequence(order_feats)   # [N, 32]
-
-# decode per-order decisions
-logits, value = model.decode_batch(ctx, global_obs)  # logits: [N, 2]
-actions = logits.argmax(dim=-1)                       # 0=inline, 1=label
+    context = model.encode_sequence(torch.zeros(3, 5))
+    logits, value = model.forward_single(context[0], torch.zeros(22))
+    action = logits.argmax(dim=-1).item()  # 0 = INLINE, 1 = LABEL
 ```
 
-Full evaluation scripts: `experiments/analyze_seq_morl.py`,
-`experiments/run_fixed_pref_eval.py`.
+For each planning event, encode its EDF-ordered pending orders once. For each
+free decision, select that order's embedding and supply the updated ten decision
+features followed by the twelve global features. Use each seed independently;
+the paper reports results across five models rather than a voting ensemble.
 
-## Training
+Full simulation and evaluation use `experiments/evaluate_policy.py` in the
+[project repository](https://fhi-git.fraunhofer.it/smartflexpack/smartflexpack_digtwin_prototype)
+(access may be required). They additionally require the separate DataGenerator
+and variants workbook, configured through `DATAGENERATOR_SRC` and
+`DATAGENERATOR_DATA`. Industrial input data are outside this model release.
 
-5 independent seeds, 200 PPO updates, 16 parallel environments,
-lr=3e-4 (Adam). ~26h CPU per seed on a 16-core machine.
+## Intended use and limitations
 
-Training code: `experiments/train_seq_morl.py`  
-Launch script: `experiments/launch_seq_morl_v5_bc.sh`
+Research on preference-conditioned packaging scheduling in the supplied DES.
+Performance depends on the plant, planner, observation transforms and operating
+assumptions used in training and evaluation. Evidence comes from simulation;
+physical deployment and service-level guarantees require further validation.
+Changing the preference vector supports different priorities within the learned
+range; coverage of every Pareto-optimal solution is unproven.
 
 ## Citation
 
 ```bibtex
-@article{venturoso2026morl,
-  title   = {Multi-Objective Reinforcement Learning for Adaptive Packaging
-             Scheduling via Sequential Attention Policy},
-  author  = {Venturoso, Leonardo and Egger, Georg and
-             Visentin, Francesco and Todescato, Marco},
-  journal = {IEEE Transactions on Industrial Informatics},
-  year    = {2026},
-  note    = {Under review}
+@unpublished{venturoso2026morl,
+  title  = {Multi-Objective Reinforcement Learning for Adaptive Packaging
+            Scheduling via Sequential Attention Policy},
+  author = {Venturoso, Leonardo and Egger, Georg and
+            Visentin, Francesco and Todescato, Marco},
+  year   = {2026},
+  note   = {Manuscript in preparation}
 }
 ```
 
 ## License
 
-[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
-Fraunhofer Italia Research, EFRE1054 Smartflexpack project
-(funded under FESR 2021–27, Priority 1 "Smart" R&I programme).
+Model weights retain the repository's [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+license. Fraunhofer Italia Research, EFRE1054 SMARTFLEXPACK project.

reinforce/seed_4/policy_200.pt → bc/bc_seq_morl.pt

@@ -1,3 +1,3 @@
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-size 52021
+oid sha256:cf488cf1bcedbf54f9a995e396b915650bec658a9a05b0fe791a9522416993bb
+size 55371

configs/paper.json

@@ -0,0 +1,46 @@
+{
+  "environment": {
+    "line_rule": "consol",
+    "inline_sec_per_pack": 0.35,
+    "max_steps": 180,
+    "step_minutes": 1440.0,
+    "max_label_lines": 5
+  },
+  "ppo": {
+    "learning_rate": 0.0003,
+    "entropy_coef": 0.01,
+    "value_coef": 0.5,
+    "clip_range": 0.2,
+    "epochs": 3,
+    "max_grad_norm": 5.0,
+    "advantage_epsilon": 0.0001
+  },
+  "training": {
+    "seed": 1,
+    "n_orders": 600,
+    "n_envs": 10,
+    "total_updates": 200,
+    "save_every": 50,
+    "tensorboard": true
+  },
+  "bc": {
+    "seed": 0,
+    "dataset_seed": 42,
+    "n_episodes": 240,
+    "n_orders": 600,
+    "n_epochs": 40,
+    "learning_rate": 0.001,
+    "val_frac": 0.1,
+    "workers": 16
+  },
+  "policy": {
+    "embed_dim": 32,
+    "hidden_dim": 64,
+    "attention_heads": 4
+  },
+  "preferences": {
+    "corner_probability": 0.5,
+    "corner_weight_low": 0.6,
+    "corner_weight_high": 0.9
+  }
+}

inference/seq_morl/common/layout.py

@@ -0,0 +1,6 @@
+"""Observation dimensions shared by the environment and the attention policy."""
+N_ORDER_FEATS = 5
+N_DECISION_FEATS = 10
+N_GLOBAL_FEATS = 12
+N_HEAD_EXTRA = N_DECISION_FEATS + N_GLOBAL_FEATS
+D_EMBED = 32

inference/seq_morl/config.py

@@ -0,0 +1,153 @@
+"""Typed experiment configuration; JSON file < CLI overrides < explicit API values."""
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field, replace
+import json
+import math
+from numbers import Integral
+from pathlib import Path
+from typing import Optional
+
+EXPERIMENTS = Path(__file__).resolve().parents[1]
+PAPER_CONFIG = EXPERIMENTS / "configs/paper.json"
+
+
+def _integers(**values):
+    for name, value in values.items():
+        if not isinstance(value, Integral) or isinstance(value, bool):
+            raise ValueError(f"{name} must be an integer")
+
+
+@dataclass(frozen=True)
+class EnvironmentConfig:
+    line_rule: str = "consol"
+    reward_scales: str = str(EXPERIMENTS / "results/reward_normalisers_consol.json")
+    inline_sec_per_pack: float = 0.35
+    max_steps: int = 180
+    step_minutes: float = 1440.0
+    max_label_lines: int = 5
+
+    def __post_init__(self):
+        _integers(max_steps=self.max_steps, max_label_lines=self.max_label_lines)
+        if self.line_rule not in {"spread", "consol", "consol:2"}:
+            raise ValueError("Unsupported line rule")
+        if self.max_steps < 1 or not math.isfinite(self.step_minutes) or self.step_minutes <= 0 or not 1 <= self.max_label_lines <= 5:
+            raise ValueError("Environment limits must be positive")
+        if not math.isfinite(self.inline_sec_per_pack) or self.inline_sec_per_pack <= 0:
+            raise ValueError("Inline speed must be finite and positive")
+
+
+@dataclass(frozen=True)
+class PPOConfig:
+    learning_rate: float = 3e-4
+    entropy_coef: float = 0.01
+    value_coef: float = 0.5
+    clip_range: float = 0.2
+    epochs: int = 3
+    max_grad_norm: float = 5.0
+    advantage_epsilon: float = 1e-4
+
+    def __post_init__(self):
+        _integers(epochs=self.epochs)
+        if self.learning_rate <= 0 or self.epochs < 1 or self.max_grad_norm <= 0:
+            raise ValueError("PPO learning rate, epochs and gradient limit must be positive")
+        if not 0 < self.clip_range < 1 or self.advantage_epsilon <= 0:
+            raise ValueError("Invalid PPO clipping or advantage normalisation")
+        if self.entropy_coef < 0 or self.value_coef < 0:
+            raise ValueError("Loss coefficients must be nonnegative")
+        if not all(math.isfinite(v) for v in asdict(self).values()):
+            raise ValueError("PPO parameters must be finite")
+
+
+@dataclass(frozen=True)
+class PolicyConfig:
+    embed_dim: int = 32
+    hidden_dim: int = 64
+    attention_heads: int = 4
+
+    def __post_init__(self):
+        _integers(embed_dim=self.embed_dim, hidden_dim=self.hidden_dim, attention_heads=self.attention_heads)
+        if min(self.embed_dim, self.hidden_dim, self.attention_heads) < 1:
+            raise ValueError("Policy dimensions must be positive")
+        if self.embed_dim % 2 or self.embed_dim % self.attention_heads:
+            raise ValueError("Embedding dimension must be even and divisible by attention_heads")
+
+
+@dataclass(frozen=True)
+class PreferenceConfig:
+    corner_probability: float = 0.5
+    corner_weight_low: float = 0.6
+    corner_weight_high: float = 0.9
+
+    def __post_init__(self):
+        if not 0 <= self.corner_probability <= 1 or not 0 <= self.corner_weight_low < self.corner_weight_high <= 1:
+            raise ValueError("Invalid preference sampling parameters")
+
+
+@dataclass(frozen=True)
+class TrainingConfig:
+    seed: int = 1
+    n_orders: int = 600
+    n_envs: int = 10
+    total_updates: int = 200
+    save_every: int = 50
+    bc_model: Optional[str] = None
+    tensorboard: bool = True
+    resume: bool = False
+
+    def __post_init__(self):
+        _integers(seed=self.seed, n_orders=self.n_orders, n_envs=self.n_envs,
+                  total_updates=self.total_updates, save_every=self.save_every)
+        if not isinstance(self.tensorboard, bool) or not isinstance(self.resume, bool):
+            raise ValueError("tensorboard and resume must be booleans")
+        if self.seed < 0 or min(self.n_orders, self.n_envs, self.total_updates, self.save_every) < 1:
+            raise ValueError("Seed must be nonnegative and training counts positive")
+
+
+@dataclass(frozen=True)
+class BCConfig:
+    seed: int = 0
+    dataset_seed: int = 42
+    n_episodes: int = 240
+    n_orders: int = 600
+    n_epochs: int = 40
+    learning_rate: float = 1e-3
+    val_frac: float = 0.1
+    workers: int = 16
+
+    def __post_init__(self):
+        _integers(seed=self.seed, dataset_seed=self.dataset_seed, n_episodes=self.n_episodes,
+                  n_orders=self.n_orders, n_epochs=self.n_epochs, workers=self.workers)
+        if min(self.seed, self.dataset_seed) < 0 or min(self.n_episodes, self.n_orders, self.n_epochs, self.workers) < 1:
+            raise ValueError("Invalid BC seeds or counts")
+        if not 0 < self.val_frac < 1 or not math.isfinite(self.learning_rate) or self.learning_rate <= 0:
+            raise ValueError("Invalid BC validation fraction or learning rate")
+
+
+@dataclass(frozen=True)
+class ExperimentConfig:
+    environment: EnvironmentConfig = field(default_factory=EnvironmentConfig)
+    ppo: PPOConfig = field(default_factory=PPOConfig)
+    policy: PolicyConfig = field(default_factory=PolicyConfig)
+    preferences: PreferenceConfig = field(default_factory=PreferenceConfig)
+    training: TrainingConfig = field(default_factory=TrainingConfig)
+    bc: BCConfig = field(default_factory=BCConfig)
+
+    @classmethod
+    def load(cls, path=None):
+        if path is None:
+            return cls()
+        data = json.loads(Path(path).read_text())
+        types = {"environment": EnvironmentConfig, "ppo": PPOConfig,
+                 "policy": PolicyConfig, "preferences": PreferenceConfig,
+                 "training": TrainingConfig, "bc": BCConfig}
+        if set(data) - set(types):
+            raise ValueError(f"Unknown config sections: {set(data) - set(types)}")
+        return cls(**{key: types[key](**value) for key, value in data.items()})
+
+    def to_dict(self):
+        return asdict(self)
+
+    def override(self, section, **values):
+        return replace(self, **{section: replace(getattr(self, section),
+                                               **{k: v for k, v in values.items() if v is not None})})

inference/seq_morl/policies/attention.py

@@ -0,0 +1,107 @@
+"""Attention actor-critic used in the paper (Fig. 1)."""
+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+from torch.distributions import Categorical
+from seq_morl.config import PolicyConfig
+
+from seq_morl.common.layout import (
+    N_ORDER_FEATS, N_HEAD_EXTRA,
+)
+
+# ---------------------------------------------------------------------------
+# Attention policy (SeqAttentionActorCritic, paper Fig. 1)
+# ---------------------------------------------------------------------------
+
+class SeqAttentionActorCritic(nn.Module):
+    """1-layer MHSA encoder over the orders competing at one planning event.
+
+    Architecture with the paper defaults:
+      order_embed: Linear(5 → D_EMBED=32) + sinusoidal positional encoding
+      encoder:     1-layer MultiheadAttention(4 heads) + residual + LayerNorm
+      head:        Linear(D_EMBED+22 → 64) → Tanh → Linear(64 → 64) → Tanh
+      output:      logits (B,2), value (B,)
+
+    The encoder is re-run at every planning event over that event's pending
+    orders, and the head additionally takes the live decision and plant
+    features, so two decisions on the same order under different plant states
+    produce different logits.
+    """
+
+    def __init__(self, config=None) -> None:
+        super().__init__()
+        self.config = config or PolicyConfig()
+        self.embed_dim = self.config.embed_dim
+        hidden = self.config.hidden_dim
+        self.order_embed = nn.Linear(N_ORDER_FEATS, self.embed_dim)
+        self.attn = nn.MultiheadAttention(self.embed_dim, num_heads=self.config.attention_heads, batch_first=True, dropout=0.0)
+        self.attn_norm = nn.LayerNorm(self.embed_dim)
+        head_in = self.embed_dim + N_HEAD_EXTRA
+        self.head = nn.Sequential(
+            nn.Linear(head_in, hidden), nn.Tanh(),
+            nn.Linear(hidden, hidden), nn.Tanh(),
+        )
+        self.value_head = nn.Linear(hidden, 1)
+        self.logits_head = nn.Linear(hidden, 2)
+
+    def _pos_enc(self, N: int, device) -> torch.Tensor:
+        pe = torch.zeros(N, self.embed_dim, device=device)
+        pos = torch.arange(N, device=device).float().unsqueeze(1)
+        idx = torch.arange(0, self.embed_dim, 2, device=device).float()
+        div = torch.exp(-idx * math.log(10000.0) / self.embed_dim)
+        pe[:, 0::2] = torch.sin(pos * div)
+        pe[:, 1::2] = torch.cos(pos * div)
+        return pe
+
+    def encode_sequence(self, order_feats: torch.Tensor) -> torch.Tensor:
+        """order_feats: (N, 5) EDF-ordered → (N, D_EMBED) contextualized embeddings."""
+        x = self.order_embed(order_feats) + self._pos_enc(order_feats.shape[0], order_feats.device)
+        x_seq = x.unsqueeze(0)  # (1, N, D_EMBED)
+        attn_out, _ = self.attn(x_seq, x_seq, x_seq)
+        return self.attn_norm(x_seq + attn_out).squeeze(0)  # (N, D_EMBED)
+
+    def decode_batch(self, ctx: torch.Tensor, extra: torch.Tensor):
+        """ctx: (B, D_EMBED), extra: (B, 22) → logits (B, 2), values (B,).
+
+        `extra` is the decision features followed by the global features.
+        """
+        h = self.head(torch.cat([ctx, extra], dim=-1))
+        return self.logits_head(h), self.value_head(h).squeeze(-1)
+
+    def forward_single(self, ctx: torch.Tensor, extra: torch.Tensor):
+        """ctx: (D_EMBED,), extra: (22,) → logits (1,2), values (1,)."""
+        return self.decode_batch(ctx.unsqueeze(0), extra.unsqueeze(0))
+
+    @torch.no_grad()
+    def predict(self, observation, deterministic=True):
+        """Gymnasium Dict observation -> (action, state), following SB3's API.
+
+        Padding is excluded before encoding, so it cannot change attention.
+        """
+        self.eval()
+        mask = torch.as_tensor(observation["order_mask"], dtype=torch.bool)
+        if not mask.any():
+            return 0, None
+        orders = torch.as_tensor(observation["orders"], dtype=torch.float32)[mask]
+        position = int(observation["position"])
+        if not 0 <= position < len(orders):
+            raise ValueError("Current order is outside the active planning event")
+        context = self.encode_sequence(orders)[position]
+        logits, _ = self.forward_single(context, torch.as_tensor(observation["extra"], dtype=torch.float32))
+        action = (torch.argmax(logits, dim=1).item() if deterministic else
+                  Categorical(logits=logits).sample().item())
+        return int(action), None
+
+
+# ---------------------------------------------------------------------------
+# Utilities
+# ---------------------------------------------------------------------------
+
+def masked_categorical(logits: torch.Tensor, label_allowed: torch.Tensor) -> Categorical:
+    """label_allowed: bool (n,); +inf trick keeps INLINE valid always."""
+    m = logits.clone()
+    m[:, 1] = m[:, 1] - 1e9 * (~label_allowed).float()
+    return Categorical(logits=m)

manifest.json

@@ -0,0 +1,59 @@
+{
+  "source_revision": "63bfad2af1379f4b09945a75490743175c095265",
+  "source_revision_role": "Current inference/reproduction code; original training revision unavailable",
+  "release_preparation": "huggingface/prepare_release.py",
+  "parameter_count": 12355,
+  "training_seeds": [
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "ppo_update": 200,
+  "checkpoint_format": "PyTorch state_dict (weights only)",
+  "validated_with_torch": "2.7.0+cu126",
+  "source_files": {
+    "README.md": "huggingface/README.md",
+    "configs/paper.json": "experiments/configs/paper.json",
+    "normalisation/feature_scales.json": "experiments/results/feature_scales.json",
+    "normalisation/reward_normalisers_consol.json": "experiments/results/reward_normalisers_consol.json",
+    "bc/bc_seq_morl.pt": "experiments/results/bc_warm_start/bc_seq_morl.pt",
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normalisation/feature_scales.json

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normalisation/reward_normalisers_consol.json

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ppo/seed_1/metrics.csv

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ppo/seed_5/policy_200.pt

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reinforce/finalize.log

@@ -1,91 +0,0 @@
-[Mon Oct 12 10:57:14 AM CEST 2026] Waiting for eval PIDs 3624262 3624264 3624266...
-[Mon Oct 12 11:43:14 AM CEST 2026] Evals done. Running analysis...
-RL rows loaded: 1800
-HEURISTIC seeds loaded: 50
-ATC seeds loaded: 20
-CR seeds loaded: 20
-CPSAT seeds loaded: 20
-
-Fixed reference point: r_cost=9439139€  r_tard=164926min  (margin=10%)
-
-======================================================================
- G6 — Hypervolume Indicator (2D: cost × tardiness)
-  Reference point: cost=9439139€  tard=164926min
-======================================================================
-
-Policy            HV mean     HV IQM                 95% CI     n
------------------------------------------------------------------
-  RL (v5)    801800321153.1132 775585790817.7950 [629539485648.4395, 977750390517.0472]    20
-  HEURISTIC  648171101006.9214 612623903920.6272 [498761456837.9420, 797727990175.1542]    20
-  ATC        659295279347.8502 656880807604.2046 [490057429738.1962, 828624859049.0122]    20
-  CR         529693033400.2584 417099305213.0255 [336872907261.2107, 746912670526.3390]    20
-  CPSAT      652400604749.8940 588975267585.3152 [482152054792.0185, 829776323108.2645]    20
-
-  Wilcoxon HV(RL) > HV(HEURISTIC): W=173.0  p=0.0047
-  Cohen's d=0.404  mean Δ=153629220146.1918
-  95% CI of Δ: [53566261726.2646, 255231725658.4940]
-  n=20 paired order-seeds
-
-  LaTeX table (copy to paper §6):
-  \begin{tabular}{lrrr}
-  Policy & HV mean & IQM & 95\% CI \\
-  \hline
-  RL & 801800321153.1132 & 775585790817.7950 & [629539485648.4395,977750390517.0472] \\
-  HEURISTIC & 648171101006.9214 & 612623903920.6272 & [498761456837.9420,797727990175.1542] \\
-  ATC & 659295279347.8502 & 656880807604.2046 & [490057429738.1962,828624859049.0122] \\
-  CR & 529693033400.2584 & 417099305213.0255 & [336872907261.2107,746912670526.3390] \\
-  CPSAT & 652400604749.8940 & 588975267585.3152 & [482152054792.0185,829776323108.2645] \\
-  \end{tabular}
-
-======================================================================
- G7 — Fixed-Preference Comparison (no post-hoc selection)
-======================================================================
-
-  Profile: cost-focused  w=(tard=0.10, cost=0.80, util=0.10)
-  Policy        cost (mean)  tard (mean)   on_time
-  --------------------------------------------------
-  RL (v5)           3436097      64139.5     0.189
-  HEURISTIC         3463585      64309.8     0.324
-  ATC               3166014      70617.6     0.185
-  CR                4374551      75553.7     0.255
-  CPSAT             3535650      64749.2     0.165
-  Wilcoxon cost(RL)<cost(HEUR): W=103.0 p=0.4782 mean_Δ=-27488€  n=20
-  TOST cost (Δ=±346359): p_lower=0.1198  p_upper=0.0854  p_TOST=0.1198  [not equiv]
-  TOST tard (Δ=±6431): p_lower=0.0513  p_upper=0.0432  p_TOST=0.0513  [not equiv]
-
-  Profile: balanced  w=(tard=0.33, cost=0.33, util=0.33)
-  Policy        cost (mean)  tard (mean)   on_time
-  --------------------------------------------------
-  RL (v5)           3554741      65225.3     0.201
-  HEURISTIC         3463585      64309.8     0.324
-  ATC               3166014      70617.6     0.185
-  CR                4374551      75553.7     0.255
-  CPSAT             3535650      64749.2     0.165
-  Wilcoxon cost(RL)<cost(HEUR): W=109.0 p=0.5653 mean_Δ=+91155€  n=20
-  TOST cost (Δ=±346359): p_lower=0.0907  p_upper=0.2142  p_TOST=0.2142  [not equiv]
-  TOST tard (Δ=±6431): p_lower=0.0487  p_upper=0.1030  p_TOST=0.1030  [not equiv]
-
-  Profile: SLA-focused  w=(tard=0.80, cost=0.10, util=0.10)
-  Policy        cost (mean)  tard (mean)   on_time
-  --------------------------------------------------
-  RL (v5)           3513426      65371.8     0.189
-  HEURISTIC         3463585      64309.8     0.324
-  ATC               3166014      70617.6     0.185
-  CR                4374551      75553.7     0.255
-  CPSAT             3535650      64749.2     0.165
-  Wilcoxon cost(RL)<cost(HEUR): W=111.0 p=0.5938 mean_Δ=+49841€  n=20
-  TOST cost (Δ=±346359): p_lower=0.0921  p_upper=0.1576  p_TOST=0.1576  [not equiv]
-  TOST tard (Δ=±6431): p_lower=0.0403  p_upper=0.1009  p_TOST=0.1009  [not equiv]
-
-======================================================================
- G8 — PPO vs REINFORCE Ablation (Hypervolume)
-======================================================================
-
-Policy              HV mean        IQM                 95% CI  seeds
---------------------------------------------------------------------
-  PPO (5 seeds)  801800321153.1132 775585790817.7950 [629539485648.4395,977750390517.0472] n=20
-  REINFORCE (2)  786008855997.1305 736524486067.7498 [611932581712.3667,964898280989.8306] n=20
-
-  Mean Δ HV (PPO−REINFORCE): 15791465155.9826 over 20 order-seeds
-  NOTE: n_training_seeds=2 for REINFORCE — no Wilcoxon (need ≥6 for p<0.05)
-[Mon Oct 12 11:43:14 AM CEST 2026] DONE.

reinforce/seed_1/policy_200.pt

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reinforce/seed_2/policy_200.pt

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reinforce/seed_3/policy_200.pt

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reinforce/seed_5/policy_200.pt

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