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bf9e424 d5ef2b7 bf9e424 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 | """MolForge environment implementation."""
from __future__ import annotations
import os
import random
from dataclasses import replace
from typing import Any, Dict, List
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from .actions import MolForgeActionMixin
from .governance import MolForgeGovernanceMixin
from .shared import (
FRAGMENT_LIBRARY,
SCENARIOS,
SLOT_ORDER,
compute_objective_score,
get_scenario,
)
from .shared import MolForgeSharedMixin
from .views import MolForgeViewMixin
try:
from ..models import GovernanceStatus, MolForgeAction, MolForgeObservation, MolForgeState, RewardComponent
except ImportError:
from models import GovernanceStatus, MolForgeAction, MolForgeObservation, MolForgeState, RewardComponent
class MolForgeEnvironment(
MolForgeActionMixin,
MolForgeGovernanceMixin,
MolForgeViewMixin,
MolForgeSharedMixin,
Environment,
):
"""Deterministic medicinal-chemistry design environment for OpenEnv."""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(self):
self._debug_state_enabled = os.getenv("MOLFORGE_DEBUG_STATE", "").lower() in {"1", "true", "yes"}
self._training_randomization_enabled = os.getenv("MOLFORGE_TRAINING_RANDOMIZATION", "").lower() in {
"1",
"true",
"yes",
}
self._reward_mode = os.getenv("MOLFORGE_REWARD_MODE", "assay_gated").lower()
self._rng = random.Random(os.getenv("MOLFORGE_RANDOM_SEED", "molforge"))
self._reset_index = -1
self._state = MolForgeState(episode_id=str(uuid4()), step_count=0)
self._scenario = SCENARIOS[0]
self._molecule: Dict[str, str] = {}
self._assay_runs: Dict[str, int] = {}
self._known_assays: List = []
self._message_log: List = []
self._history: List[Dict[str, Any]] = []
self._oracle_log: List[Dict[str, Any]] = []
self._visited_states: set[str] = set()
self._last_summary = ""
self._report_card = ""
self._reward_total = 0.0
self._restart_used = False
self._trap_penalty_active = False
self._role_metrics = self._empty_role_metrics()
self._state_path: List[str] = ["[start]"]
self._last_governance = GovernanceStatus(
status="ready",
explanation="Awaiting the first coordinated decision.",
required_roles=[],
approvals=[],
objections=[],
vetoes=[],
executable=True,
)
self.reset()
self._reset_index = -1
def reset(self) -> MolForgeObservation:
"""Start a new scenario in a deterministic rotation."""
self._reset_index += 1
self._scenario = self._select_reset_scenario()
self._molecule = dict(self._scenario.starting_scaffold)
self._assay_runs = {}
self._known_assays = []
self._message_log = []
self._history = []
self._oracle_log = []
self._visited_states = {self._molecule_signature()}
self._last_summary = "Episode initialized with a fresh multi-agent review board."
self._report_card = ""
self._reward_total = 0.0
self._restart_used = False
self._trap_penalty_active = self._scenario.trap_penalty
self._role_metrics = self._empty_role_metrics()
self._state_path = ["[start]"]
self._last_governance = GovernanceStatus(
status="ready",
explanation="Lead Chemist should propose the first coordinated action.",
required_roles=list(self._scenario.required_review_roles),
approvals=[],
objections=[],
vetoes=[],
executable=True,
)
self._state = MolForgeState(
episode_id=str(uuid4()),
step_count=0,
scenario_id=self._scenario.scenario_id,
difficulty=self._scenario.difficulty,
state_label="[start]",
state_path=list(self._state_path),
coordination_mode=self._scenario.coordination_mode, # type: ignore[arg-type]
enabled_roles=list(self._scenario.enabled_roles),
target_name=self._scenario.target_name,
current_molecule=self._molecule_signature(),
remaining_budget=self._scenario.oracle_budget,
budget_used=0,
max_budget=self._scenario.oracle_budget,
visited_states=1,
known_assay_count=0,
invalid_action_count=0,
objection_count=0,
oracle_call_count=0,
message_count=0,
decision_count=0,
submitted=False,
reward_total=0.0,
metadata={},
)
self._sync_state_metadata()
return self._build_observation(reward=0.0, done=False, reward_components=[])
def _select_reset_scenario(self):
"""Select a deterministic judge scenario or a randomized training variant."""
scenario = get_scenario(self._reset_index)
if not self._training_randomization_enabled:
return scenario
scenario = self._rng.choice(SCENARIOS)
budget_scale = self._rng.uniform(0.85, 1.15)
max_steps_delta = self._rng.choice([-1, 0, 0, 1])
starting_scaffold = dict(scenario.starting_scaffold)
if self._rng.random() < 0.35:
slot = self._rng.choice(SLOT_ORDER)
choices = [
fragment
for fragment in FRAGMENT_LIBRARY[slot]
if fragment != starting_scaffold[slot]
]
starting_scaffold[slot] = self._rng.choice(choices)
return replace(
scenario,
oracle_budget=max(1, int(round(scenario.oracle_budget * budget_scale))),
max_steps=max(4, scenario.max_steps + max_steps_delta),
starting_scaffold=starting_scaffold,
)
def step(self, action: MolForgeAction) -> MolForgeObservation: # type: ignore[override]
"""Execute one coordinated environment action."""
reward_components: List[RewardComponent] = []
done = False
error_code = ""
self._state.step_count += 1
self._state.decision_count += 1
previous_properties = self._true_properties()
previous_score = compute_objective_score(previous_properties, self._scenario)
validation_error = self._validate_action(action)
if validation_error:
error_code, message = validation_error
self._state.invalid_action_count += 1
self._last_governance = GovernanceStatus(
status="needs_revision",
explanation=message,
required_roles=list(self._scenario.required_review_roles),
approvals=[],
objections=[],
vetoes=[],
executable=False,
)
reward_components.append(
RewardComponent(
name="invalid_action",
value=-1.0,
explanation=message,
)
)
reward = -1.0
self._last_summary = message
self._append_state_label("[invalid]")
else:
governance, governance_components, policy_veto = self._assess_governance(
action, previous_properties
)
self._last_governance = governance
reward_components.extend(governance_components)
reward = sum(component.value for component in governance_components)
if policy_veto:
self._last_summary = governance.explanation
self._append_state_label("[policy_veto]")
else:
self._last_governance.status = "executed"
action_reward, done = self._execute_action(
action, reward_components, previous_properties, previous_score
)
reward += action_reward
if not done:
reward += self._evaluate_reasoning_consistency(
action,
previous_properties,
self._true_properties(),
reward_components,
)
if done and self._state.submitted:
self._append_state_label("[submitted]")
elif not done:
self._append_state_label(f"[decision_{self._state.step_count:02d}]")
if not done and self._state.step_count >= self._scenario.max_steps:
done = True
reward_components.append(
RewardComponent(
name="step_limit",
value=-0.3,
explanation="Episode ended because the maximum decision horizon was reached.",
)
)
reward -= 0.3
self._report_card = self._build_report_card(submitted=False)
self._last_summary = "Max-step termination triggered."
self._append_state_label("[terminated:max_steps]")
if not done and self._state.remaining_budget <= 0:
done = True
reward_components.append(
RewardComponent(
name="budget_exhausted",
value=-0.5,
explanation="Episode terminated because the oracle budget reached zero.",
)
)
reward -= 0.5
self._report_card = self._build_report_card(submitted=False)
self._last_summary = "Budget exhausted before a valid submission."
self._append_state_label("[terminated:budget]")
if done and not self._report_card:
self._report_card = self._build_report_card(submitted=self._state.submitted)
if done and not self._state.submitted and self._reward_mode == "curriculum":
reward += self._curriculum_terminal_progress_reward(reward_components)
reward = round(reward, 4)
self._reward_total = round(self._reward_total + reward, 4)
self._state.reward_total = self._reward_total
self._state.current_molecule = self._molecule_signature()
self._state.state_label = self._state_path[-1]
self._state.state_path = list(self._state_path)
self._state.visited_states = len(self._visited_states)
self._state.known_assay_count = len(self._known_assays)
self._state.last_error_code = error_code
self._history.append(
{
"step": self._state.step_count,
"action": action.model_dump(exclude_none=True),
"reward": reward,
"done": done,
"molecule": self._molecule_signature(),
"state_label": self._state.state_label,
"summary": self._last_summary,
"governance": self._last_governance.model_dump(),
}
)
if done:
self._report_card = self._build_report_card(submitted=self._state.submitted)
self._sync_state_metadata()
return self._build_observation(
reward=reward,
done=done,
reward_components=reward_components,
)
def _curriculum_terminal_progress_reward(self, reward_components: List[RewardComponent]) -> float:
"""Give bounded partial credit for near-miss episodes during RL warmup.
This intentionally does not change the public submission grader. It only
makes the training reward less sparse when a model builds evidence or a
chemically plausible candidate but fails to formally submit.
"""
grader_scores = self._grade_all()
progress = (
0.25 * grader_scores["candidate_score"]
+ 0.25 * grader_scores["constraint_margin_score"]
+ 0.25 * grader_scores["evidence_score"]
+ 0.15 * grader_scores["coordination_score"]
+ 0.10 * grader_scores["budget_score"]
)
progress = min(0.75, max(0.0, progress))
reward_components.append(
RewardComponent(
name="curriculum_terminal_progress",
value=round(progress, 4),
explanation=(
"Bounded warmup reward for non-submitted episodes based on candidate quality, "
"constraint margin, evidence coverage, coordination, and budget discipline. "
"Official submission_score remains 0.0 without a submit action."
),
)
)
missed_nomination_penalty = 0.0
if (
grader_scores["evidence_score"] >= 0.99
and grader_scores["constraint_margin_score"] >= 0.9
and grader_scores["candidate_score"] >= self._scenario.baseline_to_beat
):
missed_nomination_penalty = -0.25
reward_components.append(
RewardComponent(
name="curriculum_missed_nomination",
value=missed_nomination_penalty,
explanation=(
"The candidate had a strong evidence package near the decision deadline, "
"but the team failed to make a formal submit decision."
),
)
)
return progress + missed_nomination_penalty
@property
def state(self) -> MolForgeState:
"""Return the current environment state."""
return self._state
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