File size: 3,728 Bytes
2e7762d | 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 | from __future__ import annotations
import argparse
from typing import Any
import requests
def choose_action(observation: dict[str, Any]) -> dict[str, Any]:
agents = [agent for agent in observation["agents"] if agent["status"] == "idle"]
orders = [
order
for order in observation["orders"]
if order["status"] == "unassigned"
]
def manhattan(a: tuple[int, int], b: tuple[int, int]) -> int:
return abs(a[0] - b[0]) + abs(a[1] - b[1])
assignments: list[dict[str, str]] = []
used_orders: set[str] = set()
ranked_orders = sorted(
orders,
key=lambda order: (
order["deadline"],
-order["reward_value"],
),
)
for agent in agents:
agent_location = tuple(agent["location"])
best_order = None
best_score = None
for order in ranked_orders:
if order["order_id"] in used_orders:
continue
pickup = tuple(order["pickup_location"])
drop = tuple(order["drop_location"])
score = (
manhattan(agent_location, pickup)
+ manhattan(pickup, drop)
- 0.35 * float(order["reward_value"])
)
if best_score is None or score < best_score:
best_score = score
best_order = order
if best_order is None:
continue
assignments.append(
{"agent_id": agent["agent_id"], "order_id": best_order["order_id"]}
)
used_orders.add(best_order["order_id"])
return {"assignments": assignments, "rejections": []}
def main() -> None:
parser = argparse.ArgumentParser(description="Minimal Fleetmind HTTP API client.")
parser.add_argument("--base_url", default="http://127.0.0.1:8000")
parser.add_argument("--task_id", default="high_demand")
parser.add_argument("--seed", type=int, default=None)
parser.add_argument("--max_decision_steps", type=int, default=None)
args = parser.parse_args()
params = {"task_id": args.task_id}
if args.seed is not None:
params["seed"] = args.seed
if args.max_decision_steps is not None:
params["max_decision_steps"] = args.max_decision_steps
reset_response = requests.post(f"{args.base_url}/reset", params=params, timeout=30)
reset_response.raise_for_status()
observation = reset_response.json()
print(
{
"event": "reset",
"task_id": args.task_id,
"used_seed": observation["scenario_info"].get("used_seed"),
"max_decision_steps": observation.get("max_decision_steps"),
}
)
done = False
while not done:
action = choose_action(observation)
step_response = requests.post(
f"{args.base_url}/step",
json=action,
timeout=30,
)
step_response.raise_for_status()
payload = step_response.json()
observation = payload["observation"]
done = payload["done"]
print(
{
"decision_step": observation["decision_step"],
"step_reward": payload["reward"]["step_reward"],
"cumulative_reward": payload["reward"]["cumulative_reward"],
"errors": observation["feedback"]["error_summary"],
}
)
print(
{
"event": "done",
"task_id": observation["task_id"],
"used_seed": observation["scenario_info"].get("used_seed"),
"metrics": observation["metrics"],
"cumulative_reward": observation["feedback"]["cumulative_reward"],
}
)
if __name__ == "__main__":
main()
|