Before revision:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training.
After revision:  Each learned policy from our approach is robot-agnostic, which can be applied to different robotic arms. These two features can greatly reduce complexity and cost to perform robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG and makes two important improvements on prioritized experience replay to stabilize training.