This is a simple yet efficient, highly customizable grid-world implementation to run reinforcement learning algorithms.
Project description
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
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for rlgridworld-0.1004-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 40502096a2b09c38257854ba71ca37dc8c8e224b0e03cdc3c77a286fb3dff1f8 |
|
MD5 | cc1808d05d911868a3b1f09f76d81477 |
|
BLAKE2b-256 | 4424f0c24eec83292406813907b3620e217e8eb625d0f66ec74ae9eb4300919f |