Counting to Explore and Generalize in Text-based Games
- Xingdi Yuan ,
- Marc-Alexandre Côté ,
- Alessandro Sordoni ,
- Romain Laroche ,
- Remi Tachet des Combes ,
- Matthew Hausknecht ,
- Adam Trischler
European Workshop on Reinforcement Learning (EWRL) |
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.