  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.
