Events Calendar

NL Seminar-COMPREHENSIBLE CONTEXT-DRIVEN TEXT GAME PLAYING

Speaker: Xusen Yin, USC/ISI

Talk Title: COMPREHENSIBLE CONTEXT-DRIVEN TEXT GAME PLAYING

Series: Natural Language Seminar

Abstract: In order to train a computer agent to play a text based computer game, we must represent each hidden state of the game. A Long Short Term Memory LSTM model running over observed texts is a common choice for state construction. However, a normal Deep Q learning Network DQN for such an agent requires millions of steps of training or more to converge. As such, an LSTM based DQN can take tens of days to finish the training process. Though we can use a Convolutional Neural Network CNN as a text encoder to construct states much faster than the LSTM, doing so without an understanding of the syntactic context of the words being analyzed can slow convergence. In this paper, we use a fast CNN to encode position and syntax-oriented structures extracted from observed texts as states. We additionally augment the reward signal in a universal and practical manner. Together, we show that our improvements can not only speed up the process by one order of magnitude but also learn a superior agent.



Biography: Xusen Yin is a 3rd-year Ph.D. student in USC ISI, advised by Dr. Jonathan May.

Host: Xusen Yin and Jon May

More Info: https://nlg.isi.edu/nl-seminar

Webcast: https://bluejeans.com/s/qXurz

Location: Information Science Institute (ISI) - CR #689

WebCast Link: https://bluejeans.com/s/qXurz

Audiences: Everyone Is Invited

Contact: Peter Zamar

Event Link: https://nlg.isi.edu/nl-seminar