The goal of hierarchical reinforcement learning is to divide a task into different levels of coarseness with the top-level agent planning only over a high-level view of the world and each subsequent layer having a more detailed view. This paper proposes to learn a set of important states as well as their connections to each other as a high-level abstraction.
Abstract:
In many real-world scenarios, an autonomous agent often encounters various tasks within a single complex environment. We propose to build a graph abstraction over the environment structure to accelerate the learning of these tasks. Here, nodes are important points of interest (pivotal states) and edges represent feasible traversals between them. Our approach has two stages. First, we jointly train a latent pivotal state model and a curiosity-driven goal-conditioned policy in a task-agnostic manner. Second, provided with the information from the world graph, a high-level Manager quickly finds solution to new tasks and expresses subgoals in reference to pivotal states to a low-level Worker. The Worker can then also leverage the graph to easily traverse to the pivotal states of interest, even across long distance, and explore non-locally. We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Authors: Wenling Shang, Alex Trott, Stephan Zheng, Caiming Xiong, Richard Socher
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