Web: http://arxiv.org/abs/2201.08227

Sept. 16, 2022, 1:12 a.m. | Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal

cs.LG updates on arXiv.org arxiv.org

Covering option discovery has been developed to improve the exploration of
reinforcement learning in single-agent scenarios with sparse reward signals,
through connecting the most distant states in the embedding space provided by
the Fiedler vector of the state transition graph. However, these option
discovery methods cannot be directly extended to multi-agent scenarios, since
the joint state space grows exponentially with the number of agents in the
system. Thus, existing researches on adopting options in multi-agent scenarios
still rely on single-agent …

arxiv graphs reinforcement reinforcement learning tabular

More from arxiv.org / cs.LG updates on arXiv.org

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