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.

• Free software: MIT license

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

Demo

	RandomAgent	Gradient Policy Agent	Q-Learning Agent	A ctor-critic Agent
Training episodes	0	10,000	3000	3000
Reward	-70.46	49.07	198.51	284.86
Output Models	`link < ou tput/Rand omAgent>`__	link	link	`link <ou tpu t/ActorCrit icAgent>`__
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 }