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/
install with
pip install rlgridworldYou can simply use a string like
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:
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)
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']
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