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4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 db03c40 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 db03c40 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae | 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 | """Bio-Experiment Planning Environment.
Implements the OpenEnv ``Environment`` interface as a POMDP where the
agent proposes one structured experiment / analysis step at a time and
receives simulated intermediate outputs from a latent biological world.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
from models import (
ActionType,
ConclusionClaim,
ExperimentAction,
ExperimentObservation,
IntermediateOutput,
PipelineStepRecord,
ResourceUsage,
TaskSpec,
)
from server.rules.engine import RuleEngine
from server.rewards.reward import RewardBreakdown, RewardComputer
from server.simulator.latent_state import FullLatentState
from server.simulator.noise import NoiseModel
from server.simulator.transition import ACTION_COSTS, TransitionEngine, compute_action_cost
from server.tasks.generator import TaskGenerator
MAX_STEPS = 30
class BioExperimentEnvironment(Environment):
"""POMDP environment for iterative biological experiment planning.
The agent observes ``ExperimentObservation`` (partial view) while the
environment maintains a ``FullLatentState`` (hidden ground truth).
"""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(
self,
scenario_name: Optional[str] = None,
*,
domain_randomise: bool = True,
) -> None:
self._state = State(episode_id=str(uuid4()), step_count=0)
self._latent: Optional[FullLatentState] = None
self._task: Optional[TaskSpec] = None
self._scenario_name = scenario_name
self._noise = NoiseModel()
self._engine = TransitionEngine(self._noise)
self._rules = RuleEngine()
self._rewards = RewardComputer()
self._task_gen = TaskGenerator(domain_randomise=domain_randomise)
self._history: List[PipelineStepRecord] = []
self._outputs: List[IntermediateOutput] = []
self._conclusions: List[ConclusionClaim] = []
self._subagent_outputs: List[Dict[str, Any]] = []
self._discovered_markers: List[str] = []
self._candidate_mechanisms: List[str] = []
self._cumulative_reward: float = 0.0
# ── Environment interface ───────────────────────────────────────────
def reset(self, seed: Optional[int] = None) -> ExperimentObservation:
seed = seed if seed is not None else hash(uuid4()) % (2**31)
self._noise.reseed(seed)
self._state = State(episode_id=str(uuid4()), step_count=0)
self._task, self._latent = self._task_gen.generate(
seed=seed,
scenario_name=self._scenario_name,
)
self._latent.rng_seed = seed
self._history.clear()
self._outputs.clear()
self._conclusions.clear()
self._subagent_outputs.clear()
self._discovered_markers.clear()
self._candidate_mechanisms.clear()
self._cumulative_reward = 0.0
return self._build_observation(reward=0.0, done=False)
def step( # type: ignore[override]
self, action: ExperimentAction
) -> ExperimentObservation:
assert self._latent is not None, "Call reset() before step()"
assert self._task is not None
self._state.step_count += 1
prev_state = self._latent.model_copy(deep=True)
violations = self._rules.check(action, self._latent)
hard_v = self._rules.hard_violations(violations)
soft_v = self._rules.soft_violations(violations)
result = self._engine.step(
self._latent,
action,
hard_violations=hard_v,
soft_violations=soft_v,
)
self._latent = result.next_state
step_rb = self._rewards.step_reward(
action, prev_state, self._latent, result.output, hard_v, soft_v,
)
cost_budget, cost_time = compute_action_cost(action)
self._history.append(PipelineStepRecord(
step_index=self._state.step_count,
action_type=action.action_type,
method=action.method,
parameters=action.parameters,
output_summary=result.output.summary,
output_type=result.output.output_type,
success=result.output.success,
quality_score=result.output.quality_score,
resource_cost=cost_budget,
time_cost_days=cost_time,
))
self._outputs.append(result.output)
self._update_discoveries(action, result.output)
if action.action_type == ActionType.SYNTHESIZE_CONCLUSION and result.output.success:
raw_claims = action.parameters.get("claims", [])
for c in raw_claims:
if isinstance(c, dict):
self._conclusions.append(ConclusionClaim(**c))
done = result.done or self._state.step_count >= MAX_STEPS
terminal_rb = RewardBreakdown()
if done:
terminal_rb = self._rewards.terminal_reward(
self._latent,
self._conclusions,
self._task.success_criteria,
discovered_markers=self._discovered_markers,
candidate_mechanisms=self._candidate_mechanisms,
)
total_reward = step_rb.total + terminal_rb.total
self._cumulative_reward += total_reward
breakdown = step_rb.to_dict()
breakdown.update({f"term_{k}": v for k, v in terminal_rb.to_dict().items()})
return self._build_observation(
reward=total_reward,
done=done,
latest_output=result.output,
rule_violations=hard_v + soft_v,
reward_breakdown=breakdown,
metadata_extra={"reward_breakdown": breakdown},
)
@property
def state(self) -> State:
return self._state
def set_scenario(self, scenario_name: Optional[str]) -> None:
"""Set the scenario used on the next reset."""
self._scenario_name = scenario_name
# ── internal helpers ────────────────────────────────────────────────
def _build_observation(
self,
*,
reward: float,
done: bool,
latest_output: Optional[IntermediateOutput] = None,
rule_violations: Optional[List[str]] = None,
reward_breakdown: Optional[Dict[str, float]] = None,
metadata_extra: Optional[Dict[str, Any]] = None,
) -> ExperimentObservation:
assert self._task is not None
assert self._latent is not None
res = self._latent.resources
meta: Dict[str, Any] = {
"episode_id": self._state.episode_id,
"step": self._state.step_count,
"cumulative_reward": self._cumulative_reward,
}
if metadata_extra:
meta.update(metadata_extra)
return ExperimentObservation(
task=self._task,
step_index=self._state.step_count,
pipeline_history=list(self._history),
available_assays=list(self._task.available_assays),
available_tools=list(self._task.available_tools),
resource_usage=ResourceUsage(
budget_used=res.budget_used,
budget_remaining=res.budget_remaining,
time_used_days=res.time_used_days,
time_remaining_days=res.time_remaining_days,
samples_consumed=res.samples_consumed,
compute_hours_used=res.compute_hours_used,
),
latest_output=latest_output,
all_outputs=list(self._outputs),
discovered_markers=list(self._discovered_markers),
candidate_mechanisms=list(self._candidate_mechanisms),
uncertainty_summary=self._compute_uncertainty_summary(),
subagent_outputs=list(self._subagent_outputs),
conclusions=list(self._conclusions),
rule_violations=rule_violations or [],
step_reward_breakdown=reward_breakdown or {},
done=done,
reward=reward,
metadata=meta,
)
def _compute_uncertainty_summary(self) -> Dict[str, float]:
if not self._outputs:
return {}
recent = self._outputs[-5:]
avg_unc = sum(o.uncertainty for o in recent) / len(recent)
avg_qual = sum(o.quality_score for o in recent) / len(recent)
return {"avg_uncertainty": avg_unc, "avg_quality": avg_qual}
def _update_discoveries(
self, action: ExperimentAction, output: IntermediateOutput
) -> None:
if action.action_type == ActionType.MARKER_SELECTION:
markers = output.data.get("markers", [])
existing = set(self._discovered_markers)
for m in markers:
if m not in existing:
self._discovered_markers.append(m)
existing.add(m)
if action.action_type == ActionType.REGULATORY_NETWORK_INFERENCE:
regs = output.data.get("top_regulators", [])
existing = set(self._candidate_mechanisms)
for r in regs:
if r not in existing:
self._candidate_mechanisms.append(r)
existing.add(r)
if action.action_type == ActionType.PATHWAY_ENRICHMENT:
pathways = output.data.get("top_pathways", [])
existing = set(self._candidate_mechanisms)
for p in pathways:
if isinstance(p, dict) and p["pathway"] not in existing:
self._candidate_mechanisms.append(p["pathway"])
existing.add(p["pathway"])
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