In this project I applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. l first investigated the environment the agent operates in by constructing a very basic driving implementation. Once the agent was successful at operating within the environment, I then identified each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, then I implemented a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, I improved upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.