Optimal Route Finder

Transforming pathfinding with graph reinforcement learning: discover the shortest route with smarter, adaptive algorithms.

Try it out - Optimal Route Finder Demo

Enter the starting and ending nodes to find the shortest path
using graph reinforcement learning.

Source Image

Below image will be used for shortest path the nodes.

*Hover to view the image and use the mouse wheel to zoom in and out..

Here are some basic nodes combinations to find shortest path.

Nodes Detail 1

Nodes Detail 2

Nodes Detail 3

Starting node

Ending node

Note: Enter the node numbers to evaluate the shortest path from the starting node to the ending node for left image.

Features

Graph Construction

Automatically generate a graph representation of the city from CSV data. This involves reading the input data, extracting relevant information, and creating a graph structure where nodes represent locations, and edges represent routes. This foundational step sets the stage for pathfinding and analysis.

Node and Edge Definition

Define nodes (locations) and edges (routes) with relevant attributes such as distance, traffic density, and average travel time. Nodes are assigned based on specific locations within the city, while edges are characterized by parameters that influence travel, facilitating accurate modeling of real-world conditions.

Pathfinding Algorithm

Implementation of a reinforcement learning algorithm to find the shortest and fastest path between nodes. The algorithm uses historical data to train and test different routes, optimizing travel paths by considering multiple metrics like time, velocity, and traffic density to ensure efficient navigation.

Visualization

Visual representation of the city graph with highlighted shortest path. This feature involves displaying the city map with all nodes and edges, emphasizing the optimal route found by the algorithm. It provides a clear and intuitive understanding of the recommended path, aiding users in visualizing travel decisions.

Policy Gradient Methods

Implement policy gradient methods for direct policy optimization. These methods focus on improving the policy directly by optimizing the expected reward. By continuously updating the policy based on feedback from the environment, the model learns to make better pathfinding decisions, enhancing the effectiveness of the RL approach.

Data Cleaning

Automatically clean and preprocess input CSV data for consistency. This involves handling missing values, correcting data formats, and standardizing entries to ensure that the input data is accurate and reliable. Clean data is crucial for building a robust graph and training an effective reinforcement learning model.

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