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Oral
Skill Discovery for Exploration and Planning using Deep Skill Graphs
Akhil Bagaria · Jason Senthil · George Konidaris

Tue Jul 20 07:00 AM -- 07:20 AM (PDT) @
in Reinforcement Learning and Planning 2 »

We introduce a new skill-discovery algorithm that builds a discrete graph representation of large continuous MDPs, where nodes correspond to skill subgoals and the edges to skill policies. The agent constructs this graph during an unsupervised training phase where it interleaves discovering skills and planning using them to gain coverage over ever-increasing portions of the state-space. Given a novel goal at test time, the agent plans with the acquired skill graph to reach a nearby state, then switches to learning to reach the goal. We show that the resulting algorithm, Deep Skill Graphs, outperforms both flat and existing hierarchical reinforcement learning methods on four difficult continuous control tasks.

Keywords: Reinforcement Learning and Planning · Deep RL

Author Information

Akhil Bagaria (Brown University)
Jason Senthil (Brown University)
George Konidaris (Brown)

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