gym-woodoku VS gym-anytrading

Compare gym-woodoku vs gym-anytrading and see what are their differences.

gym-woodoku

It is a reinforcement learning environment for block puzzle games. (by helpingstar)

gym-anytrading

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym) (by AminHP)
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gym-woodoku gym-anytrading
1 2
3 2,027
- -
7.0 4.2
27 days ago about 2 months ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

gym-woodoku

Posts with mentions or reviews of gym-woodoku. We have used some of these posts to build our list of alternatives and similar projects.

gym-anytrading

Posts with mentions or reviews of gym-anytrading. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-08.

What are some alternatives?

When comparing gym-woodoku and gym-anytrading you can also consider the following projects:

FinRL - FinRL: Financial Reinforcement Learning. 🔥

backtesting.py - :mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.

Trading212API - An unofficial API for Trading212

Wizardry - 💫 Wizardry is an open-source CLI for building powerful algorithmic trading strategies 交易框架

forx - 💰a command line tool for checking exchange rates between currencies, both crypto and fiat.

gym-mtsim - A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning