  Reinforcement learning (RL) is always the preferred embodiment to construct
the control strategy of complex tasks, like asymmetric assembly tasks. However,
the convergence speed of reinforcement learning severely restricts its
practical application. In this paper, the convergence is first accelerated by
combining RL and compliance control. Then a completely innovative progressive
extension of action dimension (PEAD) mechanism is proposed to optimize the
convergence of RL algorithms. The PEAD method is verified in DDPG and PPO. The
results demonstrate the PEAD method will enhance the data-efficiency and
time-efficiency of RL algorithms as well as increase the stable reward, which
provides more potential for the application of RL.
