{"id":21233668,"url":"https://github.com/bdr-pro/pathfindingtensorflowrl","last_synced_at":"2026-05-01T03:35:21.007Z","repository":{"id":231754369,"uuid":"782639102","full_name":"BDR-Pro/PathFindingTensorflowRl","owner":"BDR-Pro","description":"showcases a pathfinding visualization using A* algorithm and Q-Learning model to find paths through a generated maze.","archived":false,"fork":false,"pushed_at":"2024-04-05T22:08:12.000Z","size":381,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-21T18:37:20.864Z","etag":null,"topics":["tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BDR-Pro.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-05T17:51:16.000Z","updated_at":"2024-04-05T18:19:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"403f267f-a94b-49b1-b2eb-513a1ef61243","html_url":"https://github.com/BDR-Pro/PathFindingTensorflowRl","commit_stats":null,"previous_names":["bdr-pro/pathfindingtensorflowrl"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDR-Pro%2FPathFindingTensorflowRl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDR-Pro%2FPathFindingTensorflowRl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDR-Pro%2FPathFindingTensorflowRl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDR-Pro%2FPathFindingTensorflowRl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BDR-Pro","download_url":"https://codeload.github.com/BDR-Pro/PathFindingTensorflowRl/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243676707,"owners_count":20329432,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["tensorflow"],"created_at":"2024-11-20T23:59:51.341Z","updated_at":"2026-05-01T03:35:20.913Z","avatar_url":"https://github.com/BDR-Pro.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pathfinding with A* and Q-Learning\n\nThis project showcases a pathfinding visualization using A* algorithm and Q-Learning model to find paths through a generated maze. The comparison between these two methods provides insights into their efficiency and applicability in solving pathfinding problems. Utilizing Pygame for visualization and TensorFlow for the Q-Learning model, this project offers an engaging way to understand and analyze pathfinding algorithms and machine learning in action.\n\n![alt text](Path_Finding.keras(1).png)\n\n## Features\n\n- **Maze Generation**: Randomly generates a maze for pathfinding.\n- **A* Algorithm Implementation**: Utilizes the A* algorithm to find the shortest path from start to goal.\n- **Q-Learning Model**: Employs a Q-Learning model developed with TensorFlow to learn and find paths through the maze.\n- **Visualization**: Uses Pygame for real-time visualization of the maze, paths found by A* and the Q-Learning model, start and goal positions.\n- **GPU Acceleration**: Checks for CUDA compatibility to leverage GPU acceleration for the Q-Learning model training and inference, enhancing performance.\n- **Threaded Execution**: Runs pathfinding operations in separate threads to keep the UI responsive and provide real-time updates.\n\n## Installation\n\nBefore you start, ensure you have Python 3.7+ and pip installed on your system. Clone this repository or download the code.\n\nTo install the required packages, run:\n\n```bash\npip install pygame tensorflow keras numpy\n```\n\n## Usage\n\nTo start the application, navigate to the project directory and run:\n\n```bash\npython pathfinding.py\n```\n\nThe Pygame window will open, displaying the generated maze and, eventually, the paths found by both the A* algorithm and the Q-Learning model.\n\n![alt text](\u003cلقطة شاشة 2024-04-05 034314.png\u003e)\n\n## Development\n\nThis project uses:\n\n- Python 3.7+\n- TensorFlow 2.x for the Q-Learning model.\n- Pygame for visualization.\n- NumPy for array manipulation and operations.\n\n## Contributions\n\nContributions are welcome! Whether it's bug reports, improvements, or new feature suggestions, feel free to open an issue or a pull request.\n\n---\n\nDive into the fascinating world of pathfinding algorithms and reinforcement learning with this interactive visualization project!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdr-pro%2Fpathfindingtensorflowrl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbdr-pro%2Fpathfindingtensorflowrl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdr-pro%2Fpathfindingtensorflowrl/lists"}