There are different learning algorithms to train a bot while playing a game. The main objective of this project is to implement reinforcement learning and genetic evolution algorithm to train a bot. The genetic evolution falls under hybrid method according to their goal, focus and component methodologies. In this project we are trying implement both algorithm and implement it in a custom endless runner game “Flying Deer”. In the case of genetic evolution, a generation of bots will be created and the bot plays the game. Certain fitness function will be given according to which the bot plays the game and the fittest among the generation get mutated and replaced in such a way that the bot will play the game endlessly. Similarly, in the case of reinforcement learning a single bot plays the game. The deer will be rewarded and punished with every iteration of the game. On the basis of the reward, the brain of the bot is trained to avoid the obstacles and play the game endlessly. Both the learning algorithm will be implemented which will successfully be able to learn about the game environment and play the game continuously