File size: 10,401 Bytes
4bbf0fc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 | """
test_env.py — Simulation Runner & Sanity Tests
================================================
Provides two entry-points:
run_simulation(mode) – Run one full episode and print a formatted report.
run_all() – Run all three difficulty modes and compare.
run_sanity_checks() – Fast correctness assertions (no pytest needed).
Usage
-----
python test_env.py # runs all modes + sanity checks
python test_env.py easy # run a single mode
"""
from __future__ import annotations
import sys
import builtins
from typing import Dict, Any
from env import TrafficEnv
from tasks import get_config
from baseline_agent import RuleBasedAgent
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
_COL = 80 # separator width
def _separator(char: str = "─") -> str:
return char * _COL
_ASCII_FALLBACKS = (
("\u2550", "="),
("\u2500", "-"),
("\u2502", "|"),
("\u00b7", "-"),
("\U0001F6A8", "EV"),
("\u2713", "PASS"),
("\u2717", "FAIL"),
("\u26a0\ufe0f", "WARNING"),
("\u2705", "PASS"),
("\u2014", "-"),
("\u2265", ">="),
("\u2264", "<="),
("\u2208", "in"),
)
def _safe_text(text: str) -> str:
encoding = getattr(sys.stdout, "encoding", None) or "utf-8"
try:
text.encode(encoding)
return text
except UnicodeEncodeError:
for src, dest in _ASCII_FALLBACKS:
text = text.replace(src, dest)
return text
def print(*args, **kwargs) -> None: # type: ignore[override]
"""
Safe local print wrapper:
- keeps rich Unicode output when supported
- falls back to ASCII-safe glyphs on limited encodings (e.g. cp1252)
"""
file = kwargs.get("file", sys.stdout)
if file is not sys.stdout:
builtins.print(*args, **kwargs)
return
sep = kwargs.get("sep", " ")
end = kwargs.get("end", "\n")
flush = kwargs.get("flush", False)
text = sep.join(str(arg) for arg in args)
builtins.print(_safe_text(text), end=end, flush=flush, file=file)
def _fmt_metric(key: str, value: Any) -> str:
label = key.replace("_", " ").title()
if isinstance(value, float):
return f" {label:<30} {value:.4f}"
return f" {label:<30} {value}"
# ---------------------------------------------------------------------------
# Single-mode simulation
# ---------------------------------------------------------------------------
def run_simulation(mode: str = "medium", verbose: bool = True) -> Dict[str, Any]:
"""
Run one complete episode in the specified difficulty mode.
Parameters
----------
mode : str
"easy", "medium", or "hard"
verbose : bool
Print step-by-step output if True.
Returns
-------
dict
Final info metrics plus 'cumulative_reward' and 'mode'.
"""
config = get_config(mode)
env = TrafficEnv(config)
agent = RuleBasedAgent(
min_green_time=5,
imbalance_threshold=5,
max_green_time=15,
emergency_min_green=2,
)
state = env.reset()
agent.reset()
done = False
total_reward = 0.0
step_rewards = []
if verbose:
print()
print(_separator("═"))
print(f" TRAFFIC SIGNAL SIMULATION · Mode: {mode.upper()}")
print(_separator("═"))
header = (
f"{'Step':<6} │ {'Phase':<4} │ "
f"{'N':>4} {'S':>4} {'E':>4} {'W':>4} │ "
f"{'NS':>4} {'EW':>4} │ "
f"{'Reward':>8} │ EV"
)
print(header)
print(_separator())
while not done:
action = agent.select_action(state)
next_state, reward, done, info = env.step(action)
total_reward += reward
step_rewards.append(reward)
if verbose:
phase_str = "NS" if next_state["phase"] == 0 else "EW"
ns_q = next_state["north_cars"] + next_state["south_cars"]
ew_q = next_state["east_cars"] + next_state["west_cars"]
ev_flags = next_state["emergency_flags"]
ev_active = "🚨" if any(ev_flags.values()) else " "
# Print every 5 steps, or whenever there's an emergency
if env.step_count % 5 == 0 or any(ev_flags.values()):
print(
f"{env.step_count:<6} │ {phase_str:<4} │ "
f"{next_state['north_cars']:>4} "
f"{next_state['south_cars']:>4} "
f"{next_state['east_cars']:>4} "
f"{next_state['west_cars']:>4} │ "
f"{ns_q:>4} {ew_q:>4} │ "
f"{reward:>8.3f} │ {ev_active}"
)
state = next_state
if verbose:
print(_separator())
print(f"\n FINAL METRICS ({mode.upper()})")
print(_separator())
for k, v in info.items():
print(_fmt_metric(k, v))
print(_fmt_metric("cumulative_reward", total_reward))
if step_rewards:
print(_fmt_metric("min_step_reward", min(step_rewards)))
print(_fmt_metric("max_step_reward", max(step_rewards)))
print()
result = dict(info)
result["cumulative_reward"] = total_reward
result["mode"] = mode
return result
# ---------------------------------------------------------------------------
# Run all modes and print comparison table
# ---------------------------------------------------------------------------
def run_all() -> None:
"""Run easy, medium and hard in sequence; print a comparison table."""
results = {}
for mode in ("easy", "medium", "hard"):
results[mode] = run_simulation(mode, verbose=True)
print()
print(_separator("═"))
print(" CROSS-MODE COMPARISON")
print(_separator("═"))
metrics = [
"total_cleared", "avg_waiting_time",
"max_queue_length", "signal_switch_count",
"congestion_score", "avg_ev_clear_time",
"fairness_score", "cumulative_reward",
]
col_w = 18
header = f" {'Metric':<30}" + "".join(f"{m.upper():>{col_w}}" for m in ("easy", "medium", "hard"))
print(header)
print(_separator())
for m in metrics:
row = f" {m.replace('_',' ').title():<30}"
for mode in ("easy", "medium", "hard"):
val = results[mode].get(m, "—")
if isinstance(val, float):
row += f"{val:>{col_w}.3f}"
else:
row += f"{val:>{col_w}}"
print(row)
print(_separator("═"))
print()
# ---------------------------------------------------------------------------
# Sanity / correctness checks (no external test runner needed)
# ---------------------------------------------------------------------------
def run_sanity_checks() -> None:
"""Assert basic correctness invariants for all difficulty modes."""
print()
print(_separator("═"))
print(" SANITY CHECKS")
print(_separator("═"))
passed = 0
failed = 0
def check(name: str, condition: bool) -> None:
nonlocal passed, failed
status = "✓ PASS" if condition else "✗ FAIL"
print(f" [{status}] {name}")
if condition:
passed += 1
else:
failed += 1
for mode in ("easy", "medium", "hard"):
cfg = get_config(mode)
env = TrafficEnv(cfg)
agent = RuleBasedAgent()
# 1. reset() returns valid state
state = env.reset()
agent.reset()
check(
f"[{mode}] reset() returns all-zero queues",
all(state[f"{d}_cars"] == 0 for d in ("north", "south", "east", "west")),
)
# 2. Step returns correct tuple length
action = agent.select_action(state)
result = env.step(action)
check(f"[{mode}] step() returns 4-tuple", len(result) == 4)
ns, reward, done, info = result
# 3. Reward is clipped
check(f"[{mode}] reward in [-1, 1]", -1.0 <= reward <= 1.0)
# 4. State keys present
required_keys = {
"north_cars", "south_cars", "east_cars", "west_cars",
"waiting_times", "phase", "emergency_flags", "step_count",
}
check(f"[{mode}] state has required keys", required_keys.issubset(ns.keys()))
# 5. Info keys present
required_info = {
"total_cleared", "avg_waiting_time",
"max_queue_length", "signal_switch_count",
"congestion_score", "avg_ev_clear_time",
"fairness_score",
}
check(f"[{mode}] info has required keys", required_info.issubset(info.keys()))
# 6. Queues never go negative
for _ in range(cfg["max_steps"]):
a = agent.select_action(ns)
ns, _, done, _ = env.step(a)
if done:
break
all_non_neg = all(v >= 0 for v in env.queues.values())
check(f"[{mode}] queues never go negative (full episode)", all_non_neg)
# 7. Queues never exceed max_queue
check(
f"[{mode}] queues never exceed max_queue ({cfg['max_queue']})",
all(v <= cfg["max_queue"] for v in env.queues.values()),
)
# 8. Signal phase is always 0 or 1
check(f"[{mode}] phase is always 0 or 1", env.phase in (0, 1))
# 9. total_cleared is non-negative
check(f"[{mode}] total_cleared ≥ 0", env.total_cleared >= 0)
# 10. congestion_score in [0, 1]
score = info["congestion_score"]
check(f"[{mode}] congestion_score ∈ [0, 1]", 0.0 <= score <= 1.0)
print()
print(_separator())
print(f" Results: {passed} passed, {failed} failed")
print(_separator("═"))
if failed:
print(" ⚠️ Some checks failed — review the environment logic.")
else:
print(" ✅ All sanity checks passed.")
print()
# ---------------------------------------------------------------------------
# CLI entry-point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
if len(sys.argv) == 2 and sys.argv[1].lower() in ("easy", "medium", "hard"):
run_simulation(sys.argv[1].lower(), verbose=True)
else:
run_all()
run_sanity_checks()
|