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.

There is growing consensus around the view that aligned and beneficial AI requires a reframing of objectives as being contingent, uncertain, and learnable via interaction with humans [35].

This work was supported in part by the National Science Foundation under grant CCF-2149588 and Cisco, Inc.