Observation Functions¶

In this section, we describe how to implement and extend observation functions in RouteRL. Observation functions are responsible for defining the observations available to agents interacting with the environment.

To create a new observation function, you must define a class that inherits from the Observations base class. Below, we outline the structure of the Observations class and provide details about its members and functionality:

Observations Class¶

class routerl.environment.Observations(machine_agents_list: List[Any], human_agents_list: List[Any])[source]

Abstract base class for observation functions.

Parameters:

machine_agents_list (List[Any]) – List of machine agents.
human_agents_list (List[Any]) – List of human agents.

abstractmethod observation_space() → Dict[str, Box][source]

Define the observation space for the observation function.

Returns:: Dict[str, Box] – A dictionary where keys are agent IDs and values are Gym spaces.

Default Observation Function: `PreviousAgentStart`¶

The PreviousAgentStart class serves as the default implementation for observation functions in RouteRL. This class is designed to monitor and manage the number of agents with identical origin-destination pairs and start times, operating within a predefined threshold.

Details of the PreviousAgentStart class are provided below:

class routerl.environment.observations.PreviousAgentStart(machine_agents_list: List[Any], human_agents_list: List[Any], simulation_params: Dict[str, Any], agent_params: Dict[str, Any])[source]

Observes the number of agents with the same origin-destination and start time within a threshold.

Parameters:

machine_agents_list (List[Any]) – List of machine agents.
human_agents_list (List[Any]) – List of human agents.
simulation_params (Dict[str, Any]) – Simulation parameters.
agent_params (Dict[str, Any]) – Agent parameters.

Variables:

observations (List[Any]) – List of observations.

agent_observations(agent_id: str) → ndarray[source]

Retrieve the observation for a specific agent.

Parameters:: agent_id (str) – The ID of the agent.
Returns:: np.ndarray – The observation array for the specified agent.

observation_space() → Dict[str, Box][source]

Define the observation space for each machine agent.

Returns:: Dict[str, Box] – A dictionary where keys are agent IDs and values are Gym spaces.

reset_observation() → Dict[str, ndarray][source]

Reset observations to the initial state.

Returns:: Dict[str, np.ndarray] – A dictionary of initial observations for all machine agents.

`PreviousAgentStartPlusStartTime`¶

The PreviousAgentStartPlusStartTime class is another observation functions in RouteRL. This class is designed to monitor and manage the number of agents with identical origin-destination pairs and start times, operating within a predefined threshold as well as the start time of the specific agent.

Details of the PreviousAgentStartPlusStartTime class are provided below:

class routerl.environment.observations.PreviousAgentStartPlusStartTime(machine_agents_list: List[Any], human_agents_list: List[Any], simulation_params: Dict[str, Any], agent_params: Dict[str, Any])[source]

Observes the number of agents with the same origin-destination and start time within a threshold and includes the start of the specific agent as well.