Initial commit

factory_env/env.py

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+import random
+from typing import List
+from factory_env.models import Observation, Action, Machine, Job
+
+class FactoryEnv:
+   def __init__(self, task="easy"):
+       self.task = task
+       self.time = 0
+       self.max_steps = 20
+
+   async def reset(self):
+       random.seed(42)
+
+       self.time = 0
+
+       self.machines = [
+           Machine(id="M1", status="idle"),
+           Machine(id="M2", status="idle"),
+       ]
+
+       self.jobs = [
+           Job(id="J1", remaining_time=3, deadline=10),
+           Job(id="J2", remaining_time=2, deadline=8),
+       ]
+
+       return self._get_result(0.0, False)
+
+
+   async def step(self, action: Action):
+       reward = 0.0
+
+       # Apply action
+       if action.action_type == "assign_job":
+           job = self._find_job(action.job_id)
+           machine = self._find_machine(action.machine_id)
+
+           if job and machine and machine.status == "idle":
+               job.assigned_machine = machine.id
+               machine.status = "busy"
+               machine.current_job = job.id
+               reward += 0.2
+           else:
+               reward -= 0.2  # invalid action
+
+       # Simulate time
+       self.time += 1
+
+       for machine in self.machines:
+           if machine.status == "busy":
+               job = self._find_job(machine.current_job)
+               job.remaining_time -= 1
+
+               if job.remaining_time <= 0:
+                   reward += 1.0
+                   self.jobs.remove(job)
+                   machine.status = "idle"
+                   machine.current_job = None
+
+       # Penalty for idle machines
+       idle_count = sum(1 for m in self.machines if m.status == "idle")
+       reward -= idle_count * 0.05
+
+       done = self.time >= self.max_steps or len(self.jobs) == 0
+
+       return self._get_result(reward, done)
+
+
+   def state(self):
+       return self._get_observation()
+
+   def _get_observation(self):
+       return Observation(
+           machines=self.machines,
+           pending_jobs=self.jobs,
+           time=self.time,
+       )
+
+   def _get_result(self, reward, done):
+       return type("Result", (), {
+           "observation": self._get_observation(),
+           "reward": reward,
+           "done": done
+       })
+
+   def _find_job(self, job_id):
+       return next((j for j in self.jobs if j.id == job_id), None)
+
+   def _find_machine(self, machine_id):
+       return next((m for m in self.machines if m.id == machine_id), None)
+
+   async def close(self):
+       pass
+

factory_env/grader.py

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+def compute_score(total_reward, max_possible=20):
+   score = total_reward / max_possible
+   return max(0.0, min(1.0, score))

factory_env/models.py

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+from pydantic import BaseModel
+from typing import List, Optional
+
+class Machine(BaseModel):
+   id: str
+   status: str  # idle, busy, broken
+   current_job: Optional[str] = None
+
+class Job(BaseModel):
+   id: str
+   remaining_time: int
+   deadline: int
+   assigned_machine: Optional[str] = None
+
+class Observation(BaseModel):
+   machines: List[Machine]
+   pending_jobs: List[Job]
+   time: int
+
+class Action(BaseModel):
+   action_type: str  # assign_job, wait
+   job_id: Optional[str] = None
+   machine_id: Optional[str] = None
+
+class Reward(BaseModel):
+   value: float

factory_env/tasks.py

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+TASKS = {
+   "easy": {
+       "machines": 2,
+       "jobs": 2,
+       "failures": False,
+   },
+   "medium": {
+       "machines": 3,
+       "jobs": 5,
+       "failures": True,
+   },
+   "hard": {
+       "machines": 5,
+       "jobs": 10,
+       "failures": True,
+   },
+}

inference.py

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+def parse_action(text):
+   parts = text.split()
+
+   if parts[0] == "assign_job":
+       return Action(
+           action_type="assign_job",
+           job_id=parts[1],
+           machine_id=parts[2],
+       )
+
+   return Action(action_type="wait")

openenv.yaml

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+name: factory_env
+description: Smart factory scheduling environment
+
+tasks:
+ - name: easy
+ - name: medium
+ - name: hard
+
+entry_point: factory_env.env:FactoryEnv

requirements.txt

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+pydantic
+openai
+asyncio