Retailing combines complicated communication skills and strategies to reach an agreement between buyer and seller with identical or different goals. In each transaction a good seller finds an optimal solution by considering his/her own profits while simultaneously considering whether the buyer’s needs have been met. In this paper, we manage the retailing problem by mixing cooperation and competition. We present a rich dataset of buyer-seller bargaining in a simulated marketplace in which each agent values goods and utility separately. Various attributes (preference, quality, and profit) are initially hidden from one agent with respect to its role; during the conversation, both sides may reveal, fake, or retain the information uncovered to come to a final decision through natural language. Using this dataset, we leverage transfer learning techniques on a pretrained, end-to-end model and enhance its decision-making ability toward the best choice in terms of utility by means of multi-agent reinforcement learning. An automatic evaluation shows that our approach results in more optimal transactions than human does. We also show that our framework controls the falsehoods generated by seller agents.