Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
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Abstract (arXiv)
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.
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References · 12
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2016Deep Exploration via Bootstrapped DQNcited by 518
- 2018Residual Reinforcement Learning for Robot Controlcited by 242
- 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
- 2017Parameter Space Noise for Explorationcited by 158
- 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 58
- 2018Accelerated Methods for Deep Reinforcement Learningcited by 40
- 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 37
- 2019Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assemblycited by 33
- 2021Feature-Based Compliance Control for Peg-in-Hole Assembly with Clearance or Interference Fitcited by 1
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