Gym-Trading-Env VS pyreason-gym

Compare Gym-Trading-Env vs pyreason-gym and see what are their differences.

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Gym-Trading-Env pyreason-gym
2 1
237 20
- -
4.4 7.7
21 days ago 5 months ago
Python Python
MIT License BSD 3-clause "New" or "Revised" 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-Trading-Env

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

pyreason-gym

Posts with mentions or reviews of pyreason-gym. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-16.

What are some alternatives?

When comparing Gym-Trading-Env and pyreason-gym you can also consider the following projects:

freqtrade-gym - A customized gym environment for developing and comparing reinforcement learning algorithms in crypto trading.

pyreason-rl-sim

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)

loopquest - A Production Tool for Embodied AI

dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite

rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

gym-simplegrid - Simple Gridworld Gymnasium Environment

stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

space-gym - Challenging reinforcement learning environments with locomotion tasks in space

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.