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On the connection between Bregman divergence and value in regularized Markov decision processes

2022-10-21 15:28:30
Brendan O'Donoghue
arXiv_AI
     
Abstract

In this short note we derive a relationship between the Bregman divergence from the current policy to the optimal policy and the suboptimality of the current value function in a regularized Markov decision process. This result has implications for multi-task reinforcement learning, offline reinforcement learning, and regret analysis under function approximation, among others.

Abstract (translated)

URL

https://arxiv.org/abs/2210.12160

PDF

https://arxiv.org/pdf/2210.12160.pdf

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