Web: http://arxiv.org/abs/2202.07995

June 16, 2022, 1:11 a.m. | Yihan Du, Wei Chen

cs.LG updates on arXiv.org arxiv.org

In this paper, we propose a novel Branching Reinforcement Learning (Branching
RL) model, and investigate both Regret Minimization (RM) and Reward-Free
Exploration (RFE) metrics for this model. Unlike standard RL where the
trajectory of each episode is a single $H$-step path, branching RL allows an
agent to take multiple base actions in a state such that transitions branch out
to multiple successor states correspondingly, and thus it generates a
tree-structured trajectory. This model finds important applications in
hierarchical recommendation systems …

arxiv learning lg reinforcement reinforcement learning

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