This repository contains a Reinforcement Learning (Q-learning) based simulation for modeling surge pricing and driver allocation in a multi-route transportation network. The project simulates how drivers (agents) choose routes, how surge pricing adjusts dynamically, and how drivers learn over time to maximize their earnings.
The key objective is to analyze the emergent pricing patterns, driver distribution, and reward optimization in a dynamic transportation system.
- Multi-Agent Reinforcement Learning: Each driver (agent) uses Q-learning to optimize their route selection based on historical earnings.
- Dynamic Surge Pricing: Fares adjust based on supply-demand imbalances, simulating real-world surge pricing.
- Exploration-Exploitation Tradeoff: Uses epsilon-greedy policy to balance exploration (trying new routes) and exploitation (choosing the most profitable routes).
- Data Visualization: Generates insights through plots on:
- Driver distribution across routes.
- Evolution of Q-values (profitability estimates).
- Surge pricing trends.
- Average earnings per route.
The following parameters can be adjusted to explore different scenarios:
| Parameter | Description | Default Value |
|---|---|---|
num_routes |
Number of available routes | 10 |
num_drivers |
Number of drivers (RL agents) | 20,000 |
total_riders |
Total number of riders across all routes | 300 |
iterations |
Number of simulation iterations | 100 |
base_price |
Base fare for a route | 10.0 |
k |
Surge sensitivity parameter | 0.1 |
alpha |
Learning rate for Q-value updates | 0.1 |
epsilon |
Exploration rate (epsilon-greedy policy) | 0.1 |
- Initialize Q-values: Each driver starts with zero knowledge about route profitability.
- Driver Route Selection: Each driver selects a route based on an epsilon-greedy strategy.
- Supply and Demand Calculation: The simulation calculates the number of drivers per route.
- Surge Pricing Calculation: If demand exceeds supply, fares increase using a surge multiplier.
- Reward Calculation: Driver earnings are updated based on route price and competition.
- Q-Learning Update: Drivers update their Q-values using the reward feedback.
- Iteration & Learning: The process repeats, allowing drivers to learn profitable strategies over time.
- Data Visualization: Key metrics are plotted to understand learning dynamics.
- Python 3.x
- NumPy
- Matplotlib
Clone the repository:
git clone https://github.com/viznuv/Pricing_models_dynamic.git
cd Pricing_models_dynamic