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Solving temporally-extended tasks is a challenge for most reinforcementlearning (RL) algorithms [arXiv:1906.07343]. We investigate the ability of anRL agent to learn to ask natural language questions as a tool to understand itsenvironment and achieve greater generalisation performance in novel,temporally-extended environments. We do this by endowing this agent with theability of asking "yes-no" questions to an all-knowing Oracle. This allows theagent to obtain guidance regarding the task at hand, while limiting the accessto new information. To study the emergence of such natural language questionsin the context of temporally-extended tasks we first train our agent in aMini-Grid environment. We then transfer the trained agent to a different,harder environment. We observe a significant increase in generalisationperformance compared to a baseline agent unable to ask questions. Throughgrounding its understanding of natural language in its environment, the agentcan reason about the dynamics of its environment to the point that it can asknew, relevant questions when deployed in a novel environment.

Ask Before You Act: Generalising to Novel Environments by Asking Questions