Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction

Understand

Text-based games are a natural challenge domain for deep reinforcement learning algorithms.

• Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback.
• In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone.
• Our design recognizes and takes advantage of the structural characteristics of text-based games.