  Deep reinforcement learning (RL) algorithms frequently require prohibitive
interaction experience to ensure the quality of learned policies. The
limitation is partly because the agent cannot learn much from the many
low-quality trials in early learning phase, which results in low learning rate.
Focusing on addressing this limitation, this paper makes a twofold
contribution. First, we develop an algorithm, called Experience Grafting (EG),
to enable RL agents to reorganize segments of the few high-quality trajectories
from the experience pool to generate many synthetic trajectories while
retaining the quality. Second, building on EG, we further develop an AutoEG
agent that automatically learns to adjust the grafting-based learning strategy.
Results collected from a set of six robotic control environments show that, in
comparison to a standard deep RL algorithm (DDPG), AutoEG increases the speed
of learning process by at least 30%.
