AAAI Publications, 2018 AAAI Spring Symposium Series

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Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space
Ermo Wei, Drew Wicke, Sean Luke

Last modified: 2018-03-15


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.


Reinforcement Learning; Deep Learning; Parameterized Action MDP

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