Title: Hierarchical Policy Search via Return-Weighted Density Estimation,
Abstract: Learning an optimal policy from a multi-modal reward function is a
challenging problem in reinforcement learning (RL). Hierarchical RL (HRL)
tackles this problem by learning a hierarchical policy, where multiple option
policies are in charge of different strategies corresponding to modes of a
reward function and a gating policy selects the best option for a given
context. Although HRL has been demonstrated to be promising, current
state-of-the-art methods cannot still perform well in complex real-world
problems due to the difficulty of identifying modes of the reward function. In
this paper, we propose a novel method called hierarchical policy search via
return-weighted density estimation (HPSDE), which can efficiently identify the
modes through density estimation with return-weighted importance sampling. Our
proposed method finds option policies corresponding to the modes of the return
function and automatically determines the number and the location of option
policies, which significantly reduces the burden of hyper-parameters tuning.
Through experiments, we demonstrate that the proposed HPSDE successfully learns
option policies corresponding to modes of the return function and that it can
be successfully applied to a challenging motion planning problem of a redundant
robotic manipulator.