Taxi Decoration Prediction Model

This model predicts whether a taxi image is decorated or undecorated based on input image data. It was trained using a convolutional neural network (CNN) architecture on a custom dataset of decorated and undecorated taxi images.

Taxi-Q-Learning Model

This is a Q-learning model trained to solve the Taxi-v3 environment. The model uses a reinforcement learning approach to optimize the agent's policy for navigating the taxi environment.

Model Details

• Model Type: Q-Learning
• Environment: OpenAI Gym's Taxi-v3
• Training Algorithm: Q-learning
• Input: The state of the taxi environment, including the position of the taxi, destination, and status of the passenger.
• Output: Optimal action based on the current state.

Intended Use

This model is intended to solve the Taxi-v3 environment, a simple reinforcement learning task where the goal is to pick up and drop off passengers at the correct locations.

How to Use:

You can use this model for reinforcement learning tasks or to further train it in different environments.

from huggingface_hub import hf_hub_download
import pickle

# Download the model
model_path = hf_hub_download(repo_id="willco-afk/taxi", filename="q-learning.pkl")

# Load the Q-learning model
with open(model_path, "rb") as f:
    q_learning_model = pickle.load(f)

# Use the model for your task