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Provable Benefits of Representational Transfer in Reinforcement Learning
[article]
2023
arXiv
pre-print
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a target task. We propose a new notion of task relatedness between source and target tasks, and develop a novel approach for representational transfer under this assumption. Concretely, we show that given generative access to source tasks, we can discover a representation, using which
arXiv:2205.14571v2
fatcat:f6p2jiwnnzg3ll2lzagiyas5yu