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An OpenAI Gym for Shopping Cart Reinforcement Learning.
This is a project by Winder Research, a Cloud-Native Data Science consultancy.
Installation
pip install gym-shopping-cart
Usage
This example will use the small example data included in the repo.
import gym
import gym_shopping_cart
env = gym.make("ShoppingCart-v0")
episode_over = False
rewards = 0
while not episode_over:
state, reward, episode_over, _ = env.step(env.action_space.sample())
print(state, reward)
rewards += reward
print("Total reward: {}".format(rewards))
Real Shopping Cart Data
This environment uses real shopping cart information from the Instacart dataset.
To help read this data the library also comes with a data parser. This loads the raw data and cleans the data to be in a format expected by the environment.
Credits
Gitlab icon made by Freepik from www.flaticon.com.
1 maintainer
Metadata
1 maintainer
Release files for gym-shopping-cart 0.2.0
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| File | Size | Uploaded | Provenance | Actions |
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gym_shopping_cart-0.2.0-py3-none-any.whl | Python 3 | none | any | Details for gym_shopping_cart-0.2.0-py3-none-any.whl |
Total release size: 50.0 kB
Release files / gym_shopping_cart-0.2.0.tar.gz
| Download URL | gym_shopping_cart-0.2.0.tar.gz |
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Release files / gym_shopping_cart-0.2.0-py3-none-any.whl
| Download URL | gym_shopping_cart-0.2.0-py3-none-any.whl |
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| Size | 25.9 kB |
| Tags | Python 3 |
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