GymProxy is a tiny library for porting an external python-based simulation on Gymnasium (Gym) environment.
It is designed for users who want to apply reinforcement learning (RL) in an existing python-based simulation.
GymProxy makes a target simulation environment inter-operate with Gym through multi-threading.
As pre-requisite, you should have Python 3.7+ installed on your machine.
NumPy and Gym libraries are also required.
Clone this repository on your machine and run:
$ cd ~/projects/gymproxy # We assume that the repository is cloned to this directory
$ pip install .
If you use Anaconda, you can install GymProxy by the followings:
$ conda activate my_env # We assume that 'my_env' is your working environment
$ conda develop ~/projects/gymproxy
We present three gym-type environments as usage examples of GymProxy:
- CarRental
- GamblersProblem
- AccessControlQueue
Each of the above environments simulates example 4.2 (Jack's car rental), 4.3 (gambler's problem), and 10.2 (access-control queuing task), respectively, described in this book:
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press, 2018.
This work was supported in part by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2024-00392332, Development of 6G Network Integrated Intelligence Plane Technologies & 2017-0-00045, Hyper-Connected Intelligent Infrastructure Technology Development), and part by Electronics and Telecommunications Research Institutue (ETRI) grant funded by the Korea government (No. 24ZR1100, A Study of Hyper-Connected Thinking Internet Technology by Autonomous Connecting, Controlling and Evolving Ways).