As aforementioned, reinforcement learning (RL) is leveraged to bridge the gap between training and inference of NMT, by directly optimizing the evaluation measure (e.g., BLEU) at training time.
nMT model can be viewed as an agent, which interacts with the environment (the previous words y <t and the context vector z available at each step t).