Counting to Explore and Generalize in Text-based Games
This work addresses the challenge of exploration and generalization in text-based environments for reinforcement learning, showing incremental improvement over prior methods.
The researchers tackled the problem of learning policies in text-based games by proposing a recurrent RL agent with episodic exploration, which achieved generalization to unseen games of greater difficulty.
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.