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# 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.
"""
Data models for SUMO-RL Environment.
This module defines the Action, Observation, and State types for traffic
signal control using SUMO (Simulation of Urban MObility).
"""
from typing import Dict, List, Optional
from openenv.core.env_server import Action, Observation, State
from pydantic import Field
class SumoAction(Action):
"""
Action for SUMO traffic signal control environment.
Represents selecting which traffic light phase to activate next.
Attributes:
phase_id: Index of the green phase to activate (0 to num_phases-1)
ts_id: Traffic signal ID (for multi-agent support, default "0")
"""
phase_id: int
ts_id: str = "0"
class SumoObservation(Observation):
"""
Observation from SUMO traffic signal environment.
Contains traffic metrics for decision-making.
Attributes:
observation: Flattened observation vector containing:
- One-hot encoded current phase
- Min green flag (binary)
- Lane densities (normalized)
- Lane queues (normalized)
observation_shape: Shape of observation for reshaping
action_mask: List of valid action indices
sim_time: Current simulation time in seconds
done: Whether episode is complete
reward: Reward from last action (None on reset)
metadata: Additional info (system metrics, etc.)
"""
observation: List[float] = Field(default_factory=list)
observation_shape: List[int] = Field(default_factory=list)
action_mask: List[int] = Field(default_factory=list)
sim_time: float = 0.0
done: bool = False
reward: Optional[float] = None
metadata: Dict = Field(default_factory=dict)
class SumoState(State):
"""
State of SUMO traffic signal environment.
Tracks both configuration and runtime state.
Configuration attributes:
net_file: Path to SUMO network file (.net.xml)
route_file: Path to SUMO route file (.rou.xml)
num_seconds: Total simulation duration in seconds
delta_time: Seconds between agent actions
yellow_time: Duration of yellow phase in seconds
min_green: Minimum green time per phase in seconds
max_green: Maximum green time per phase in seconds
reward_fn: Name of reward function used
Runtime attributes:
episode_id: Unique episode identifier
step_count: Number of steps taken in episode
sim_time: Current simulation time in seconds
total_vehicles: Total number of vehicles in simulation
total_waiting_time: Cumulative waiting time across all vehicles
"""
# Episode tracking
episode_id: str = ""
step_count: int = 0
# SUMO configuration
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"
# Runtime metrics
sim_time: float = 0.0
total_vehicles: int = 0
total_waiting_time: float = 0.0
mean_waiting_time: float = 0.0
mean_speed: float = 0.0
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