and obtains the corresponding answer.

[@hu2018playing] define their action space as the set of possible questions and the state as a distribution over the possible characters. Their state update mechanism uses statistics of people’s responses to questions. In addition, as a non-zero reward is obtained only at the end of the episode (corresponding to game win / lose), they augment their environment with a reward network, generating an intrinsic immediate reward at every time step, whose goal is to predict the true reward.

[@chen2018learning] use an Long Short Term Memory (LSTM) state update mechanism, so that the state space is the hidden space of the LSTM network. They use a naive-Bayes mechanism for making predictions at the end of each episode. In addition, their approach consists of two major elements: a DQN RL agent who plays the game and a knowledge acquisition mechanism, whose goal is to estimate answers distributions to questions (regardless of the specific episode being played), which utilizes a matrix decomposition mechanism.

[@zhao2016towards] propose a RL-based dialogue state tracking system, which they apply to the 20 questions game. Their approach combines reward signal with label supervision, which is related to our approach, as will be explained in 