| Definition | GraphEnvs is a library for reinforcement learning applied to graph optimization problems like shortest path, TSP, and MST. |
| No of environments | 9 |
GraphEnvs is a library designed for reinforcement learning (RL) applied to graph-based problems. It offers various environments that model well-known graph optimization tasks such as finding the shortest path, solving the traveling salesman problem (TSP), and constructing minimum spanning trees (MST). These environments are built using PyTorch and PyTorch Geometric, making them suitable for training RL agents on graph-related challenges. The library supports multiple environments, each with different objectives and action spaces, such as selecting nodes or edges, with rewards based on the specific graph problem’s constraints and goals.
The library is structured into two main categories: GraphEnvs-Basic and GraphEnvs-Extended. Basic environments include classic graph problems like Shortest Path, Steiner Tree, and TSP, where the agent’s goal is to find optimal paths, cycles, or vertex covers. Extended environments introduce more complex scenarios such as MultiCast Routing and Distribution Center Selection, where the agent must optimize solutions under additional constraints like distance or delivery times. These environments provide a versatile framework for experimenting with RL algorithms in the context of graph theory and combinatorial optimization, allowing researchers and developers to train agents on problems that are critical in fields like logistics, transportation, and network design.