  Deep reinforcement learning trains neural networks using experiences sampled
from the replay buffer, which is commonly updated at each time step. In this
paper, we propose a method to update the replay buffer adaptively and
selectively to train a robot arm to accomplish a suction task in simulation.
The response time of the agent is thoroughly taken into account. The state
transitions that remain stuck at the boundary of constraint are not stored. The
policy trained with our method works better than the one with the common replay
buffer update method. The result is demonstrated both by simulation and by
experiment with a real robot arm.
