Action Branching Architectures for Deep Reinforcement Learning

Understand

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.