This repository implements a chatbot trained using deep reinforcement learning techniques. The system uses a seq2seq model with shared-weight LSTM encoder-decoder architecture for sequence-to-sequence learning. After initial seq2seq training, policy gradient reinforcement learning is applied to improve response generation quality based on a reward function, allowing the chatbot to generate more engaging responses.