  Discrete-action algorithms have been central to numerous recent successes of
deep reinforcement learning. However, applying these algorithms to
high-dimensional action tasks requires tackling the combinatorial increase of
the number of possible actions with the number of action dimensions. This
problem is further exacerbated for continuous-action tasks that require fine
control of actions via discretization. In this paper, we propose a novel neural
architecture featuring a shared decision module followed by several network
branches, one for each action dimension. This approach achieves a linear
increase of the number of network outputs with the number of degrees of freedom
by allowing a level of independence for each individual action dimension. To
illustrate the approach, we present a novel agent, called Branching Dueling
Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network
(Dueling DDQN). We evaluate the performance of our agent on a set of
challenging continuous control tasks. The empirical results show that the
proposed agent scales gracefully to environments with increasing action
dimensionality and indicate the significance of the shared decision module in
coordination of the distributed action branches. Furthermore, we show that the
proposed agent performs competitively against a state-of-the-art continuous
control algorithm, Deep Deterministic Policy Gradient (DDPG).

Machine Learning;Artificial Intelligence