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Discovering Options for Exploration by Minimizing Cover Time

Yuu Jinnai · Jee Won Park · David Abel · George Konidaris

Pacific Ballroom #117

Keywords: [ Theory and Algorithms ]


One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is bounded by the expected cover time of a random walk over the graph induced by the MDP's transition dynamics. We therefore propose to accelerate exploration by constructing options that minimize cover time. We introduce a new option discovery algorithm that diminishes the expected cover time by connecting the most distant states in the state-space graph with options. We show empirically that the proposed algorithm improves learning in several domains with sparse rewards.

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