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Inverse reinforcement learning in contextual MDPs
2021
Machine Learning
AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously
doi:10.1007/s10994-021-05984-x
fatcat:6ep2uiwwsfgq7orqqs3xdmtbs4