Spaces:
Running
Running
File size: 8,725 Bytes
6fac95b 429558a 6fac95b 429558a 6fac95b 429558a 6fac95b | 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 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
SUMO-RL Environment Server Implementation.
This module wraps the SUMO-RL SumoEnvironment and exposes it
via the OpenEnv Environment interface for traffic signal control.
"""
import os
import uuid
from typing import Any, Dict
# Set SUMO_HOME before importing sumo_rl
os.environ.setdefault("SUMO_HOME", "/usr/share/sumo")
from openenv.core.env_server import Action, Environment, Observation
# Support both in-repo and standalone imports
try:
# In-repo imports (when running from OpenEnv repository)
from ..models import SumoAction, SumoObservation, SumoState
except ImportError as e:
if "relative import" not in str(e) and "no known parent package" not in str(e):
raise
# Standalone imports (when running via uvicorn server.app:app)
from models import SumoAction, SumoObservation, SumoState
# Import SUMO-RL
try:
from sumo_rl import SumoEnvironment as BaseSumoEnv
except ImportError as e:
raise ImportError(
"sumo-rl is not installed. Please install it with: pip install sumo-rl"
) from e
def _json_safe(value: Any) -> Any:
"""Convert library-specific values into JSON-serializable Python types."""
if value is None or isinstance(value, (str, int, float, bool)):
return value
if isinstance(value, dict):
return {str(key): _json_safe(item) for key, item in value.items()}
if isinstance(value, (list, tuple, set)):
return [_json_safe(item) for item in value]
tolist = getattr(value, "tolist", None)
if callable(tolist):
try:
return tolist()
except Exception:
pass
item = getattr(value, "item", None)
if callable(item):
try:
return item()
except Exception:
pass
return str(value)
class SumoEnvironment(Environment):
"""
SUMO-RL Environment wrapper for OpenEnv.
This environment wraps the SUMO traffic signal control environment
for single-agent reinforcement learning.
Args:
net_file: Path to SUMO network file (.net.xml)
route_file: Path to SUMO route file (.rou.xml)
num_seconds: Simulation duration in seconds (default: 20000)
delta_time: Seconds between agent actions (default: 5)
yellow_time: Yellow phase duration in seconds (default: 2)
min_green: Minimum green time in seconds (default: 5)
max_green: Maximum green time in seconds (default: 50)
reward_fn: Reward function name (default: "diff-waiting-time")
sumo_seed: Random seed for reproducibility (default: 42)
Example:
>>> env = SumoEnvironment(
... net_file="/app/nets/single-intersection.net.xml",
... route_file="/app/nets/single-intersection.rou.xml"
... )
>>> obs = env.reset()
>>> print(obs.observation_shape)
>>> obs = env.step(SumoAction(phase_id=1))
>>> print(obs.reward, obs.done)
"""
def __init__(
self,
net_file: str,
route_file: str,
num_seconds: int = 20000,
delta_time: int = 5,
yellow_time: int = 2,
min_green: int = 5,
max_green: int = 50,
reward_fn: str = "diff-waiting-time",
sumo_seed: int = 42,
):
"""Initialize SUMO traffic signal environment."""
super().__init__()
# Store configuration
self.net_file = net_file
self.route_file = route_file
self.num_seconds = num_seconds
self.delta_time = delta_time
self.yellow_time = yellow_time
self.min_green = min_green
self.max_green = max_green
self.reward_fn = reward_fn
self.sumo_seed = sumo_seed
# Create SUMO environment (single-agent mode)
# Key settings:
# - use_gui=False: No GUI in Docker
# - single_agent=True: Returns single obs/reward (not dict)
# - sumo_warnings=False: Suppress SUMO warnings
# - out_csv_name=None: Don't write CSV files
self.env = BaseSumoEnv(
net_file=net_file,
route_file=route_file,
use_gui=False,
single_agent=True,
num_seconds=num_seconds,
delta_time=delta_time,
yellow_time=yellow_time,
min_green=min_green,
max_green=max_green,
reward_fn=reward_fn,
sumo_seed=sumo_seed,
sumo_warnings=False,
out_csv_name=None, # Disable CSV output
add_system_info=True,
add_per_agent_info=False,
)
# Initialize state
self._state = SumoState(
net_file=net_file,
route_file=route_file,
num_seconds=num_seconds,
delta_time=delta_time,
yellow_time=yellow_time,
min_green=min_green,
max_green=max_green,
reward_fn=reward_fn,
)
self._last_info = {}
def reset(self) -> Observation:
"""
Reset the environment and return initial observation.
Returns:
Initial SumoObservation for the agent.
"""
# Reset SUMO simulation
obs, info = self.env.reset()
# Update state tracking
self._state.episode_id = str(uuid.uuid4())
self._state.step_count = 0
self._state.sim_time = 0.0
# Store info for metadata
self._last_info = info
return self._make_observation(obs, reward=None, done=False, info=info)
def step(self, action: Action) -> Observation:
"""
Execute agent's action and return resulting observation.
Args:
action: SumoAction containing the phase_id to execute.
Returns:
SumoObservation after action execution.
Raises:
ValueError: If action is not a SumoAction.
"""
if not isinstance(action, SumoAction):
raise ValueError(f"Expected SumoAction, got {type(action)}")
# Validate phase_id
num_phases = self.env.action_space.n
if action.phase_id < 0 or action.phase_id >= num_phases:
raise ValueError(
f"Invalid phase_id: {action.phase_id}. "
f"Valid range: [0, {num_phases - 1}]"
)
# Execute action in SUMO
# Returns: (obs, reward, terminated, truncated, info)
obs, reward, terminated, truncated, info = self.env.step(action.phase_id)
done = terminated or truncated
# Update state
self._state.step_count += 1
self._state.sim_time = info.get("step", 0.0)
self._state.total_vehicles = info.get("system_total_running", 0)
self._state.total_waiting_time = info.get("system_total_waiting_time", 0.0)
self._state.mean_waiting_time = info.get("system_mean_waiting_time", 0.0)
self._state.mean_speed = info.get("system_mean_speed", 0.0)
# Store info for metadata
self._last_info = info
return self._make_observation(obs, reward=reward, done=done, info=info)
@property
def state(self) -> SumoState:
"""Get current environment state."""
return self._state
def _make_observation(
self, obs: Any, reward: float, done: bool, info: Dict
) -> SumoObservation:
"""
Create SumoObservation from SUMO environment output.
Args:
obs: Observation array from SUMO environment
reward: Reward value (None on reset)
done: Whether episode is complete
info: Info dictionary from SUMO environment
Returns:
SumoObservation for the agent.
"""
# Convert observation to list
if hasattr(obs, "tolist"):
obs_list = obs.tolist()
else:
obs_list = list(obs)
# Get action mask (all actions valid in SUMO-RL)
num_phases = self.env.action_space.n
action_mask = list(range(num_phases))
# Extract system metrics for metadata
system_info = {k: v for k, v in info.items() if k.startswith("system_")}
# Create observation
return SumoObservation(
observation=obs_list,
observation_shape=[len(obs_list)],
action_mask=action_mask,
sim_time=info.get("step", 0.0),
done=done,
reward=reward,
metadata={
"num_green_phases": num_phases,
"system_info": _json_safe(system_info),
},
)
|