Year: 2003
Type: article
Abstract: Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning (PGRL) methods have received recent attention as a means to solve problems with continuous state spaces. However, they su#er from slow convergence. In this paper, we combine these two approaches and propose a family of hierarchical policy gradient algorithms for problems with continuous state and/or action spaces. We also introduce a class of hierarchical hybrid algorithms, in which a group of subtasks, usually at the higher-levels of the hierarchy, are formulated as value function-based RL (VFRL) problems and the others as PGRL problems. We demonstrate the performance of our proposed algorithms using a simple taxi-fuel problem and a complex continuous state and action ship steering domain.
Authors: Mohammad Ghavamzadeh, Sridhar Mahadevan
Institution: University of Massachusetts Amherst
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2139394304