instances/02_Maze_Solver_Q_Learning_Gridworld_RL.json

{
    "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
}