RLGridWorld

This is a simple yet efficient, highly customizable grid-world implementation to run reinforcement learning algorithms.

The official documentation is here https://rlgridworld.readthedocs.io/ <https://rlgridworld.readthedocs.io/>_

install with

.. code-block:: bash

pip install rlgridworld

Environment

You can simply use a string like

.. code-block:: text

W H T O W
W O O H W
W O A O W
W O O T W
W W W W W

to represent a grid-world, where

* A: Agent
* T: Target location
* O: Empty Ground spot (where the agent can step on and stay)
* W: Wall
* H: Hole (where the agent will fall if it steps in)

The single_rgb_array rendering of which is:

.. image:: imgs/ExampleFile.png :width: 400 :alt: Alternative text

The goal of the agent is to reach one of the Target locations without falling into a hole or falling out of the edge. (More pre-configured environments can be found in EnvSettings)

Actions

The actions can be continuous or discrete. The agent can also move diagonally. The details can be found in the Action class in rlgridworld/gridenv.py

* Continuous: Action is a tuple of length 2, where the first element is the x-axis and the second element is the y-axis
* Discrete: Action can be chosen from ['UP', 'DOWN', 'RIGHT', 'LEFT', 'UPRIGHT', 'UPLEFT', 'DOWNRIGHT', 'DOWNLEFT']

Reward

Customizable with r_fall_off, r_reach_target, r_timeout, r_continue. The details can be found in the init function of class GridEnv in rlgridworld/gridenv.py