RTFM: Generalising to Novel Environment Dynamics via Reading

Understand

Obtaining policies that can generalise to new environments in reinforcement learning is challenging.

• In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
• We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations.
• We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information.