This is a simulation driven by two separate Reinforcement Learning algorithms called Q-learning and SARSA. The simulation includes an agent which tries to learn the shortest, safest path from a given position to a certain point in a grid. The user is able to compare the algorithms using data generated during the simulation and evaluate their effectiveness and performance. The environment and RL agent are programmed in pure C++ whereas the graphs are generated using Python.
A video demonstration of the program can be found at https://youtu.be/h4inhJ41yXM.