In this project we applied different reinforcement learning algorithms and policies which include imitation learning, DQN, DQFD, and AC3 to the Pommerman FFA competition challenge. We were able to successfully perform as efficiently as SimpleAgent which was a baseline heuristic using DQN and an architecture inspired by AlphaGo and Atari papers. Most of our agents emerged with defensive behaviors where we tried to train them further with reward shaping to observe emergence of other behaviors.