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Leveraginginsights from theRobustRLliterature, wepropose arobustMCEIRLalgorithm, which is a principled approach to help with this mismatch. Finally, we empirically demonstrate the stable performance of our algorithm compared to the standard MCEIRL algorithm under transition dynamics mismatches in both finite and continuousMDPproblems.

With the ability to learn concepts and sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, wecanacquire temporally-extended behaviors.

As training data become larger, distributed training has been broadly adopted in machine learning tasks.