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Complex, multi-task problems have proven to be difficult to solve efficientlyin a sparse-reward reinforcement learning setting. In order to be sampleefficient, multi-task learning requires reuse and sharing of low-levelpolicies. To facilitate the automatic decomposition of hierarchical tasks, wepropose the use of step-by-step human demonstrations in the form of naturallanguage instructions and action trajectories. We introduce a dataset of suchdemonstrations in a crafting-based grid world. Our model consists of ahigh-level language generator and low-level policy, conditioned on language. Wefind that human demonstrations help solve the most complex tasks. We also findthat incorporating natural language allows the model to generalize to unseentasks in a zero-shot setting and to learn quickly from a few demonstrations.Generalization is not only reflected in the actions of the agent, but also inthe generated natural language instructions in unseen tasks. Our approach alsogives our trained agent interpretable behaviors because it is able to generatea sequence of high-level descriptions of its actions.

Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning