Nowadays, learning to play games has been one of the popular topics researched in AI. Learning to play games using game theory/ search algorithms require careful domain specific feature definitions, making them averse to scalability. The goal of this project is to develop a more general framework to learn game specific features and solve the problem. The game I am considering for this project is the popular board game- Checkers. Since the environment of Checkers is deterministic, there is no need of using neural network to learn the environment. This problem was solved using naive RL implementation but required good feature definitions to set up the problem. The variation of reinforcement learning algorithm that was used is Qlearning. Here the main aim was to create a Q-learning agent and to make it learn to play game of checkers by playing it against search algorithm. Minimax algorithm was used to train the Qlearning agent. The Q-learning AI agent was trained by playing multiple games with Mini-Max agent. The Qlearning AI agent won only 29 games but was able to draw 19,500 games while playing 50,000 games with Mini-Max agent.