utilizing contextual information to generate accurate and relevant responses, and implementing strategies to make conversations human-like. We propose a supervised learning approach for model development and make use of a dataset consisting of multi-turn conversations for model training. In particular, we first develop a module based on deep reinforcement learning to maximize the utilization of contextual information serving as insurance for accurate response generation. Key Words: chatbot, reinforcement learning, sequence to sequence, generative adversarial nets