Language Understanding for Text-based Games using Deep Reinforcement Learning

Appeared on the arXiv, June 2015.

The joint work of Karthik Narasimhan, Tejas Kulkarni and Regina Barzilay.

The aim of the paper is to create an autonomous agent that solves quests in text-based adventure games. The agent has no knowledge of the underlying game state, and must decide upon what action to take based only upon the representation of the game state that is afforded by the game. In this sense it seeks to solve a similar problem to that of the now famous Atari deep learning paper. This is also an interesting model for how humans communicate with one another.

There are similarities in approach, moreover, in that both employ reinforcement learning. In contrast, this paper employs a Long-Short Term Memory network.

They use Evennia, a Python framework for building multiplayer online text games (used here in a single player context).

Adriaan S.: Q-learning does not scale well. (This could account for the small vocabulary used.)

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