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While Reinforcement Learning ( RL) has made great strides towards solvingincreasingly complicated problems, many algorithms are still brittle to evenslight environmental changes. Contextual Reinforcement Learning (cRL) providesa framework to model such changes in a principled manner, thereby enablingflexible, precise and interpretable task specification and generation. Our goalis to show how the framework of cRL contributes to improving zero-shotgeneralization in RL through meaningful benchmarks and structured reasoningabout generalization tasks. We confirm the insight that optimal behavior in cRLrequires context information, as in other related areas of partialobservability. To empirically validate this in the cRL framework, we providevarious context-extended versions of common RL environments. They are part ofthe first benchmark library, CARL, designed for generalization based on cRLextensions of popular benchmarks, which we propose as a testbed to furtherstudy general agents. We show that in the contextual setting, even simple RLenvironments become challenging - and that naive solutions are not enough togeneralize across complex context spaces.

Contextualize Me -- The Case for Context in Reinforcement Learning