This is the second lab in the series of 3 lab projects designed to introduce Multi-Agent Systems (MAS) as a base for Machine Learning. Note that these series do not feature any Machine Learning, only MAS. I completed these labs in my OO Programming, Data Structures, and Algorithms class.
Here is a short description of what exactly this program does:
Purpose: Creates 2 agents that navigate a grid-like environment with doors. Agents start in their respective initial states and must navigate to the final state of the environment
Grid/Environment: Generated based on rows, columns
Movement: Depending on the environment, thw two agents have different probabilities/strategies for movement. Agents can move up, down, left, right. or stay still. They cannot move outside the bounds of the grid.
Returns the average number and optimal number of steps these agents take in the environment to reach the final state. Also returns the win counter for each agent as well the counter for how many times both agents won together