Human Learning and Decision-Making Models¶

RouteRL provides a catalog of human learning and decision-making models, including three discrete choice models. These models, popular within transportation community, emulate human agents as utility maximizers, where individual utilities are influenced by individual characteristics—unlike reinforcement learning algorithms that primarily focus on cost minimization.

Note

Users can create their own human models by inheriting BaseLearningModel.

Random Model¶

Gawron Model¶

Weighted Average Model¶

Base Learning Model¶

class routerl.human_learning.learning_model.BaseLearningModel[source]

This is an abstract base class for the learning models used to model human learning and decision-making.

Users can create their own learning models by inheriting from this class.

abstractmethod act(state) → None[source]

Method to select an action based on the current state and cost.

Returns:: None

abstractmethod learn(state, action, reward) → None[source]

Method to learn the model based on the current state and cost.

Parameters:

state (Any) – The current state of the environment.
action (Any) – The action to take.
reward (Any) – The reward received from the environment.

Returns:

None