# Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks

arXiv: https://arxiv.org/abs/2104.04078 · HTML: https://mlsanity.com/paper/2104.04078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Emergence of Locomotion Behaviours in Rich Environments](https://mlsanity.com/paper/1707.02286/) (2017)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards](https://mlsanity.com/paper/1906.05841/) (2019)
- [Accelerated Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1803.02811/) (2018)
- [Deep Reinforcement Learning for High Precision Assembly Tasks](https://mlsanity.com/paper/1708.04033/) (2017)
- [Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly](https://mlsanity.com/paper/1903.01066/) (2019)
- [Feature-Based Compliance Control for Peg-in-Hole Assembly with Clearance or Interference Fit](https://mlsanity.com/paper/2103.16003/) (2021)

## 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.