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92107a5 | 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 | import os
from typing import Optional
import numpy as np
from .world import World, Intersection
STATE_DIM = 14
MIN_GREEN = 6
DECISION_INTERVAL = 5
class DQNController:
def __init__(self, weights_path: str):
data = np.load(weights_path)
self.feat_w1 = data["feature.0.weight"]
self.feat_b1 = data["feature.0.bias"]
self.feat_w2 = data["feature.2.weight"]
self.feat_b2 = data["feature.2.bias"]
self.val_w1 = data["value.0.weight"]
self.val_b1 = data["value.0.bias"]
self.val_w2 = data["value.2.weight"]
self.val_b2 = data["value.2.bias"]
self.adv_w1 = data["advantage.0.weight"]
self.adv_b1 = data["advantage.0.bias"]
self.adv_w2 = data["advantage.2.weight"]
self.adv_b2 = data["advantage.2.bias"]
def forward(self, states: np.ndarray) -> np.ndarray:
x = states @ self.feat_w1.T + self.feat_b1
x = np.maximum(x, 0)
x = x @ self.feat_w2.T + self.feat_b2
x = np.maximum(x, 0)
v = x @ self.val_w1.T + self.val_b1
v = np.maximum(v, 0)
v = v @ self.val_w2.T + self.val_b2
a = x @ self.adv_w1.T + self.adv_b1
a = np.maximum(a, 0)
a = a @ self.adv_w2.T + self.adv_b2
q = v + (a - a.mean(axis=-1, keepdims=True))
return q
def select_actions(self, states: np.ndarray) -> np.ndarray:
q = self.forward(states)
return q.argmax(axis=-1)
def _queue_norm(world: World, rid: Optional[str]) -> float:
if not rid or rid not in world.roads:
return 0.0
r = world.roads[rid]
return min(1.0, r.queue_at_tail() / max(1, r.length))
def _occ_norm(world: World, rid: Optional[str]) -> float:
if not rid or rid not in world.roads:
return 0.0
r = world.roads[rid]
return min(1.0, r.occupancy() / max(1, r.length))
def extract_state(world: World, I: Intersection) -> np.ndarray:
s = np.zeros(STATE_DIM, dtype=np.float32)
s_rid = I.incoming.get("S")
w_rid = I.incoming.get("W")
s[0] = _queue_norm(world, s_rid)
s[1] = _queue_norm(world, w_rid)
s[2] = _occ_norm(world, s_rid)
s[3] = _occ_norm(world, w_rid)
phase = I.current_phase()
s[4] = 1.0 if frozenset({"N", "S"}) == phase else 0.0
s[5] = min(1.0, I.phase_timer / 45.0)
s[6] = 1.0 if I.phase_timer >= MIN_GREEN else 0.0
s_neighbors = []
w_neighbors = []
for nid in I.neighbors:
NI = world.intersections.get(nid)
if NI is None:
continue
s_neighbors.append(_queue_norm(world, NI.incoming.get("S")))
w_neighbors.append(_queue_norm(world, NI.incoming.get("W")))
s[7] = float(np.mean(s_neighbors)) if s_neighbors else 0.0
s[8] = float(np.mean(w_neighbors)) if w_neighbors else 0.0
e_rid = I.outgoing.get("E")
n_rid = I.outgoing.get("N")
s[9] = _occ_norm(world, e_rid)
s[10] = _occ_norm(world, n_rid)
total_occ = 0.0
total_cap = 0.0
for d in ["S", "W"]:
rid = I.incoming.get(d)
if rid and rid in world.roads:
total_occ += world.roads[rid].occupancy()
total_cap += world.roads[rid].length
s[11] = total_occ / max(1, total_cap)
s_q = world.roads[s_rid].queue_at_tail() if s_rid and s_rid in world.roads else 0
w_q = world.roads[w_rid].queue_at_tail() if w_rid and w_rid in world.roads else 0
s[12] = (s_q - w_q) / max(1.0, float(s_q + w_q))
s[13] = world.tick / max(1, world.horizon)
return s
def rl_step(controller: DQNController, world: World) -> None:
inter_ids = sorted(world.intersections.keys())
states = np.stack([extract_state(world, world.intersections[iid]) for iid in inter_ids])
actions = controller.select_actions(states)
for i, iid in enumerate(inter_ids):
I = world.intersections[iid]
if I.preempt_direction is not None:
continue
if actions[i] == 1 and I.phase_timer >= MIN_GREEN:
I.current_phase_idx = (I.current_phase_idx + 1) % len(I.phases)
I.phase_timer = 0
_controller_cache: Optional[DQNController] = None
def get_controller() -> Optional[DQNController]:
global _controller_cache
if _controller_cache is not None:
return _controller_cache
for candidate in [
os.path.join(os.path.dirname(__file__), "..", "..", "dqn_weights.npz"),
os.path.join(os.path.dirname(__file__), "..", "..", "..", "dqn_weights.npz"),
"/app/env/dqn_weights.npz",
"dqn_weights.npz",
]:
if os.path.exists(candidate):
_controller_cache = DQNController(candidate)
return _controller_cache
return None
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