Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph

Xin Ye; Yezhou Yang

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arXiv · 2103.01350

Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph

Xin Ye·Yezhou Yang

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Abstract

We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical process that facilitates: 1) the high-level network raising a sub-goal towards achieving a designated final goal; 2) the low-level network towards an optimal policy; and 3) the overall system generalizing unseen environments and goals. We evaluate our approach with two settings of partially observable goal-driven tasks -- a grid-world domain and a robotic object search task. Our experimental results show that our approach exhibits superior generalization performance on both unseen environments and new goals.

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BibTeX RIS

Xin Ye, Yezhou Yang. 2021-03-01. Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph. https://arxiv.org/abs/2103.01350

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