The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search
Summary: arXiv:2603.23873v1 Announce Type: new
Abstract: DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic functions that guide heuristic search algorithms tailored to deep neural networks (DNNs). This innovative software leverages the latest advances in deep reinforcement learning, heuristic search, and formal logic.
Key Features of DeepXube
The DeepXube package incorporates a variety of advanced techniques and functionalities designed to enhance the efficiency and effectiveness of solving pathfinding problems. The key features include:
- Limited-Horizon Bellman-Based Learning: This approach allows for optimizing decision-making over a finite future, enhancing the learning of heuristic functions.
- Hindsight Experience Replay: This technique improves learning efficiency by allowing the model to learn from past experiences as if they were successful.
- Batched Heuristic Search: DeepXube enables the execution of heuristic searches in batches, significantly speeding up the pathfinding process.
- Answer-Set Programming for Goal Specification: Users can define complex goals using formal logic, improving the flexibility and precision of pathfinding solutions.
- Multiple-Inheritance Structure: This robust feature simplifies the definition of various pathfinding domains and facilitates the generation of relevant training data.
- Automatic Parallelization: DeepXube efficiently manages the generation of training data across CPUs and reinforcement learning updates across GPUs, enhancing computational speed.
- Integration with Advanced Pathfinding Algorithms: Users can easily implement algorithms like batch weighted A*, Q* search, and beam search to tackle complex pathfinding challenges via command-line arguments.
- Visualization and Monitoring Tools: The software includes features for visualizing results, profiling code, and monitoring progress during both training and problem-solving phases.
Accessibility and Community
DeepXube is designed for accessibility, being available as a free and open-source tool. Researchers, developers, and enthusiasts can access the software through its GitHub repository at https://github.com/forestagostinelli/deepxube. This open-source nature fosters community engagement, enabling users to contribute to its development, suggest features, and report issues.
Conclusion
DeepXube stands at the intersection of machine learning and heuristic search, providing a powerful tool for automating the solution of pathfinding problems. Its incorporation of state-of-the-art techniques and features makes it a valuable resource for anyone looking to leverage deep neural networks in pathfinding applications. With a commitment to openness and community collaboration, DeepXube is poised to advance the field of automated pathfinding solutions.