Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
This addresses convergence speed limitations for RL applications in complex robotic assembly tasks, though it appears incremental.
The paper tackles slow convergence in reinforcement learning for asymmetric assembly tasks by combining RL with compliance control and proposing a progressive extension of action dimension (PEAD) mechanism, showing enhanced data-efficiency, time-efficiency, and stable reward in DDPG and PPO algorithms.
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.