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{
"name": "02_Maze_Solver_Q_Learning_Gridworld_RL",
"query": "Can you help me create a system to solve maze-style Gridworld tasks using the Q-learning algorithm? The system should use numpy to make the core calculations more efficient and matplotlib for visualizations. The Q-learning algorithm should be implemented in `src/train.py`, and the aptly-named Gridworld environment should be implemented in `src/env.py` in such a way that one could specific the grid size and start/end positions when instantiating it. The system needs to record the learning curve during training, tracking episodes and their corresponding returns, and save it as `results/figures/learning_curve.png`. Additionally, I'd like you to visualize and save the paths taken by the agent in each episode in a file called `results/figures/path_changes.gif`, and save the trained model as `models/saved_models/q_learning_model.npy`. It would be great to have some form of real-time feedback during training, like seeing the progress or getting updates on how the model is learning. Also, if you can, please try and write the code in a way that's easy to modify or extend later on.",
"tags": [
"Reinforcement Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Q-learning\" algorithm is used in `src/train.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [],
"criteria": "The \"Gridworld\" environment is defined in `src/env.py` with the ability for a user to specify a grid size and start/end positions.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0,
1
],
"criteria": "Learning curves are recorded during training, and saved as `results/figures/learning_curve.png`. Episodes and returns are recorded.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
1,
2
],
"criteria": "The learned model is saved as `models/saved_models/q_learning_model.npy`.",
"category": "Save Trained Model",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
0,
1
],
"criteria": "Paths taken during learning are visualized and saved as `results/figures/path_changes.gif`.",
"category": "Visualization",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "Some real-time progress or feedback during the training process should be displayed.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The code should be written in a way that's easy to modify or extend later on.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |