Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space
Machine Learning
2018-10-24 v1 Artificial Intelligence
Machine Learning
Abstract
We explore Deep Reinforcement Learning in a parameterized action space. Specifically, we investigate how to achieve sample-efficient end-to-end training in these tasks. We propose a new compact architecture for the tasks where the parameter policy is conditioned on the output of the discrete action policy. We also propose two new methods based on the state-of-the-art algorithms Trust Region Policy Optimization (TRPO) and Stochastic Value Gradient (SVG) to train such an architecture. We demonstrate that these methods outperform the state of the art method, Parameterized Action DDPG, on test domains.
Cite
@article{arxiv.1810.09656,
title = {Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space},
author = {Ermo Wei and Drew Wicke and Sean Luke},
journal= {arXiv preprint arXiv:1810.09656},
year = {2018}
}
Comments
Accepted in AAAI 18 Spring Symposium