Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space

Ermo Wei; Drew Wicke; Sean Luke

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arXiv · 1810.09656

Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space

Ermo Wei·Drew Wicke·Sean Luke

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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.

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BibTeX RIS

Ermo Wei, Drew Wicke, Sean Luke. 2018-10-23. Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space. https://arxiv.org/abs/1810.09656

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