This match combines artificial intelligence and machine learning to create a unique concept of idle matchplay that mimics classic snake and maze puzzles. Players can set up their robot to run automatic simulations using advanced algorithms such as Baseline Random, Classic Heuristics, Classic Search (BFS, DFS), A*, Monte Carlo methods, DQN and many more. The match is powered by the popular machine learning engine TensorFlow and allows participants to train their own neural network from scratch, fine-tune parameters, and upgrade AI capabilities with earned chips. The match also features a global leaderboard where participants can compete against each other to achieve the ultimate high tally. Overall, the match is designed to provide a fun and engaging experience for participants who enjoy artificial intelligence and machine learning concepts.
Navigate maze with artificial intelligence and machine learning!
Unique Game StyleControl AI to play the game for you. Experience the unique concept of idle game play that mimic classic snake and maze puzzle solver.AI Auto PlaySetup AI for your robot and enjoy watching idle gameplay running automatic simulation. Unlocking more advanced algorithms for your robot. Choose from Baseline Random, Classic Heuristics, Classic Search (BFS, DFS), A*, Monte Carlo methods, DQN and many more to be added.Training Your Own ModelTrain your own neural network from scratch. This game is powered by the popular machine learning engine Tensorflow. Experience what .....
Characteristics:
- Focus on artificial intelligence and machine learning concepts
- Distinctive idle matchplay concept
- Wide range of advanced algorithms to choose from
- Ability to train your own neural network
- Customizable parameters and upgradable AI capabilities
- Global leaderboard for competition among participants
What's New in the Latest Version 1.3.52
Last updated on Feb 24, 2023
Minor Update 1.3.52- Fixed wrong parameters used for saved DQN models
- Fixed wrong Q and loss values being displayed when switching between DQN models
- Reduced max initial epsilon for DQN to avoid logs not being generated
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