This research paper investigates the use of deep reinforcement learning (DRL) algorithms to train artificial agents to play the strategy game So Long Sucker (SLS). The authors developed a simplified version of the game, with the goal of making it more suitable for machine learning. They then tested three different DRL algorithms, DQN, DDQN, and Dueling DQN, to see how well they could teach agents the rules of the game and develop winning strategies. While the agents were successful in learning the game's rules, they required extensive training and still made occasional mistakes. This highlights the challenges of using DRL to teach agents complex, social, and adversarial games. The paper also provides a publicly available version of the game for future research on negotiation and coalition formation in multi-agent learning environments.