# Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation

arXiv: https://arxiv.org/abs/1907.06884 · HTML: https://mlsanity.com/paper/1907.06884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Goal-Conditioned Reinforcement Learning in the Presence of an Adversary](https://mlsanity.com/paper/2211.06929/) (2022)

## Abstract (arXiv)

Deep reinforcement learning trains neural networks using experiences sampled from the replay buffer, which is commonly updated at each time step. In this paper, we propose a method to update the replay buffer adaptively and selectively to train a robot arm to accomplish a suction task in simulation. The response time of the agent is thoroughly taken into account. The state transitions that remain stuck at the boundary of constraint are not stored. The policy trained with our method works better than the one with the common replay buffer update method. The result is demonstrated both by simulation and by experiment with a real robot arm.