lunarlander_gym

Summary

This project is implementation of multiple AI agents based on different Reinforcement Learning methods to OpenAI Gymnasium Lunar-Lander environment which is classic rocket landing trajectory optimization problem.

• Documentation: https://lunarlander-gym.readthedocs.io.

Demo

	Q-Learning Agent	Actor-critic Agent
Training episodes	3000	3000
Reward	198.51	284.86
Output Models	link	link
Demo

	RandomAgent	Gradient Policy Agent
Training episodes	0	10,000
Reward	-70.46	49.07
Output Models	link	link
Demo

Installation

From sources

The sources for lunarlander_gym can be downloaded from the Github repo_.

• Clone the repository

      $ git clone git://github.com/ehsan2754/lunarlander_gym
  

• Once you have a copy of the source, you can install it with:

      $ sudo apt update && sudo apt upgrade
      $ sudo apt install make
      $ pip install -r requirements_dev.txt
      $ sudo make install
  
  

• Now you can just immidiately use it:

      $ lunarlander-gym -h
          usage: lunarlander_gym [-h] -m M
  
          options:
          -h, --help        show this help message and exit
          -m M, --method M  Specifies the Reinforcement Agent method { 0 -> Random, 1 ->
                              Gradient based optimization, 2 -> Q-Learning Agent 3 -> Actor-
                              critic }