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Hierarchical Representation Learning for Markov Decision Processes
[article]
2021
arXiv
pre-print
In this paper we present a novel method for learning hierarchical representations of Markov decision processes. Our method works by partitioning the state space into subsets, and defines subtasks for performing transitions between the partitions. We formulate the problem of partitioning the state space as an optimization problem that can be solved using gradient descent given a set of sampled trajectories, making our method suitable for high-dimensional problems with large state spaces. We
arXiv:2106.01655v2
fatcat:bzje2xuahreg3f255rk2zgaagu