Behavior fusion for deep reinforcement learning.

For deep reinforcement learning (DRL) system, it is difficult to design a reward function for complex tasks, so this paper proposes a framework of behavior fusion for the actor-critic architecture, which learns the policy based on an advantage function that consists of two value functions. Firstly, the proposed method decomposes a complex task into several sub-tasks, and merges the trained policies for those sub-tasks into a unified policy for the complex task, instead of designing a new reward function and training for the policy. Each sub-task is trained individually by an actor-critic algorithm using a simple reward function.