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We explore Deep Reinforcement Learning in a parameterized action space.Specifically, we investigate how to achieve sample-efficient end-to-endtraining in these tasks. We propose a new compact architecture for the taskswhere the parameter policy is conditioned on the output of the discrete actionpolicy. We also propose two new methods based on the state-of-the-artalgorithms Trust Region Policy Optimization (TRPO) and Stochastic ValueGradient (SVG) to train such an architecture. We demonstrate that these methodsoutperform the state of the art method, Parameterized Action DDPG, on testdomains.

Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space