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nextdataAI: a Python library for solving mazes using classical algorithms (A*, BFS, DFS, Dijkstra, Greedy), reinforcement learning (QLearning, QTable, QNN, QLSTM), and novel pseudo-heuristics. Features include a neural network-based heuristic approximating Manhattan distance (NNHeuristic) and a custom SManhattan heuristic, a dataset gener

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Name		Name	Last commit message	Last commit date
Latest commit History 71 Commits
.idea		.idea
Papers		Papers
TestsAnimations		TestsAnimations
nextdataAI		nextdataAI
.gitignore		.gitignore
Final_Notebook_Magoni.html		Final_Notebook_Magoni.html
Final_Notebook_Magoni.ipynb		Final_Notebook_Magoni.ipynb
Final_Notebook_Magoni.pdf		Final_Notebook_Magoni.pdf
Main.py		Main.py
README.md		README.md
requirements.txt		requirements.txt
results.csv		results.csv
results.pkl		results.pkl
results_all.csv		results_all.csv
setup.py		setup.py

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Artificial Intelligence Foundamentals

pip install nextdataAI

Generate some Mazes
Implement course solutions
- BFS
- DFS
- Greedy
- A*
  - Manhattan Heuristic
  - Euclidean Heuristic
  - Overkill Heuristic that destroys both Manhattan and Euclidean
- Dijkstra
- Genetic Algorithm
DL/RL approaches
- Q-Learning
  - Q-Learning with NN and Experience Replay
  - Q-Learning with LSTM and Experience Replay
- A* with NN heuristic
- A* with LSTM heuristic
- A* with CNN heuristic
Evaluation Metrics
- Time
- Number of Nodes Expanded
- Number of Nodes in the Path
- Number of Nodes in the Solution
- Number of Nodes in the Maze

Heuristics	Manhattan	Euclidean	NNHeuristic
Implemented	✅	✅	✅

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nextdataAI: a Python library for solving mazes using classical algorithms (A*, BFS, DFS, Dijkstra, Greedy), reinforcement learning (QLearning, QTable, QNN, QLSTM), and novel pseudo-heuristics. Features include a neural network-based heuristic approximating Manhattan distance (NNHeuristic) and a custom SManhattan heuristic, a dataset gener

ai nethack astar-algorithm maze pathfinding dqn dfs bfs dijkstra-algorithm minihack

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