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tasks/task1_easy.py

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+"""Task 1 — Feasibility Check (Easy).
+
+The agent observes a scheduling instance (jobs, machines, proposed assignments)
+and must respond with "feasible" or "infeasible" to indicate whether all
+scheduling constraints are satisfied.
+
+Grading: exact match — 1.0 if correct, 0.1 if wrong, 0.0 if empty.
+Max steps per episode: 3.
+Expected agent accuracy: ~90%.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from environment import INSTANCE_BANK, SchedulingOptEnv
+from graders.grader_detection import FeasibilityGrader
+from models import Action
+
+TASK_ID = "feasibility_check"
+MAX_STEPS = 3
+DIFFICULTY = "easy"
+
+
+def run_episode(env: SchedulingOptEnv, agent_fn: Any) -> dict[str, Any]:
+    """Run a single feasibility-check episode.
+
+    Args:
+        env: An initialized SchedulingOptEnv instance.
+        agent_fn: A callable that receives an Observation and returns a
+                  response string ("feasible" or "infeasible").
+
+    Returns:
+        Episode summary dict with total reward and step count.
+    """
+    obs = env.reset(task_id=TASK_ID)
+    total_reward = 0.0
+    steps = 0
+    info: dict[str, Any] = {}
+
+    for _ in range(MAX_STEPS):
+        response = agent_fn(obs)
+        action = Action(response=response, task_id=TASK_ID)
+        obs, reward, done, info = env.step(action)
+        total_reward += reward
+        steps += 1
+        if done:
+            break
+
+    return {
+        "task": TASK_ID,
+        "difficulty": DIFFICULTY,
+        "steps": steps,
+        "total_reward": round(total_reward, 4),
+        "info": info,
+    }
+
+
+def get_all_instances_with_answers() -> list[dict[str, Any]]:
+    """Return instance bank entries relevant to feasibility check."""
+    return [
+        {
+            "instance": entry["instance"],
+            "is_feasible": entry["is_feasible"],
+            "description": entry["description"],
+        }
+        for entry in INSTANCE_BANK
+    ]