This is the code for this video on Youtube by Siraj Raval as part of the move 37 course at School of AI. GridWorld is a test-bed frame for learning about Markov Decision Processes. This project is meant to be a tool for anyone to get started learning about Markov Decision Processes.
- Python 3
tkinderfor the UI
The main program to run is main.py. This must be supplied with a JSON configuration file to build your GridWorld. See example.json for a rudimentary configuration.
Example Usage:
python3 main.py example.jsonClick the buttons to step through value iteration or progress in increments of 100. The policy current is shown as arrows at each cell.
The map descriptor for GridWorld is in the JSON configuration file given to main.py on startup. The JSON configuration defines the following parameters:
| Parameter | Type | Description |
|---|---|---|
width |
int > 0 |
width of GridWorld in cells |
height |
int > 0 |
height of GridWorld in cells |
initial_value |
float |
initial value to populate all values with |
discount |
0 ≤ float ≤ 1 |
discounting factor for compute future expected rewards |
living_cost |
float ≤ 0 |
cost subtracted from each value at each iteration |
obstacles |
list of indices (list) |
cells that we consider to be non-traversable obstacles |
transition_distribution |
dict with floats for "forward", "left","right","backward" |
transition function; adjacent tiles only; must sum to 1! |
terminals |
list of objects with "state" (index list) and "reward" (float) |
terminal states and their associated rewards |
There is no input validation so unexpected behavior will occur for unreasonable values, e.g., a negative width or height. YOU HAVE BEEN WARNED!
This project is licensed under the MIT License. In other words, do whatever you want with it! Just remember to give appropriate credit :-)
Credits for this code go to mohitd. I've merely created a wrapper to get people started.