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.
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.
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.
You can use this model for reinforcement learning tasks or to further train it in different environments.
1from huggingface_hub import hf_hub_download
2import pickle
3
4# Download the model
5model_path = hf_hub_download(repo_id="willco-afk/taxi", filename="q-learning.pkl")
6
7# Load the Q-learning model
8with open(model_path, "rb") as f:
9 q_learning_model = pickle.load(f)
10
11# Use the model for your task
