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While reinforcement learning algorithms can learn effective policies forcomplex tasks, these policies are often brittle to even minor task variations,especially when variations are not explicitly provided during training. Onenatural approach to this problem is to train agents with manually specifiedvariation in the training task or environment. However, this may be infeasiblein practical situations, either because making perturbations is not possible,or because it is unclear how to choose suitable perturbation strategies withoutsacrificing performance. The key insight of this work is that learning diversebehaviors for accomplishing a task can directly lead to behavior thatgeneralizes to varying environments, without needing to perform explicitperturbations during training. By identifying multiple solutions for the taskin a single environment during training, our approach can generalize to newsituations by abandoning solutions that are no longer effective and adoptingthose that are. We theoretically characterize a robustness set of environmentsthat arises from our algorithm and empirically find that our diversity-drivenapproach can extrapolate to various changes in the environment and task.

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL