A Survey of Text Games for Reinforcement Learning informed by Natural Language

Reinforcement Learning (RL) has shown human-level performance in solving complex, single setting virtual environments Mnih et al. [2013] & Silver et al. [2016]. However, applications and theory in RL problems have been far less developed and it has been posed that this is due to a wide divide between the empirical methodology associated with virtual environments in RL research and the challenges associated with reality Dulac-Arnold et al. [2019]. Simply put, Text Games provide a safe and data efficient way to learn from environments that mimic language found in real-world scenarios Shridhar et al. [2020]. Natural language (NL) has been introduced as a solution to many of the challenges in RL Luketina et al. [2019], as NL can facilitate the transfer of abstract knowledge to downstream tasks. However, RL approaches on these language driven environments are still limited in their development and therefore a call has been made for an improvement on the evaluation settings where language is a first-class component. Text Games gained wider acceptance as a testbed for NL research following work Figure 1: Sample gameplay from Narasimhan et al. [2015] who leveraged the Deep Q Network (DQN) framework from a fantasy Text Game as for policy learning on a set of synthetic textual games. Text Games are both partially given by Narasimhan et al. observable (as shown in Figure 1) and include outcomes that make reward signals [2015] where the player takes simple to define, making them a suitable problem for Reinforcement Learning to the action'Go East' to cross solve. However, research so far has been performed independently, with many authors the bridge.