# DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future

arXiv: https://arxiv.org/abs/2105.13806 · HTML: https://mlsanity.com/paper/2105.13806/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Unsupervised Learning for Physical Interaction through Video Prediction](https://mlsanity.com/paper/1605.07157/) (2016)
- [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://mlsanity.com/paper/1605.02097/) (2016)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://mlsanity.com/paper/1604.07255/) (2016)
- [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](https://mlsanity.com/paper/1511.03791/) (2015)
- [Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning](https://mlsanity.com/paper/1606.02560/) (2016)
- [Deep Reinforcement Learning With Macro-Actions](https://mlsanity.com/paper/1606.04615/) (2016)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [SimpleDS: A Simple Deep Reinforcement Learning Dialogue System](https://mlsanity.com/paper/1601.04574/) (2016)
- [Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control](https://mlsanity.com/paper/1604.08382/) (2016)
- [Deep Reinforcement Learning Discovers Internal Models](https://mlsanity.com/paper/1606.05174/) (2016)
- [Negative Learning Rates and P-Learning](https://mlsanity.com/paper/1603.08253/) (2016)

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

- [MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning](https://mlsanity.com/paper/2412.11410/) (2024)

## Abstract (arXiv)

Combination of machine learning (for generating machine intelligence), computer vision (for better environment perception), and robotic systems (for controlled environment interaction) motivates this work toward proposing a vision-based learning framework for intelligent robot control as the ultimate goal (vision-based learning robot). This work specifically introduces deep reinforcement learning as the the learning framework, a General-purpose framework for AI (AGI) meaning application-independent and platform-independent. In terms of robot control, this framework is proposing specifically a high-level control architecture independent of the low-level control, meaning these two required level of control can be developed separately from each other. In this aspect, the high-level control creates the required intelligence for the control of the platform using the recorded low-level controlling data from that same platform generated by a trainer. The recorded low-level controlling data is simply indicating the successful and failed experiences or sequences of experiments conducted by a trainer using the same robotic platform. The sequences of the recorded data are composed of observation data (input sensor), generated reward (feedback value) and action data (output controller). For experimental platform and experiments, vision sensors are used for perception of the environment, different kinematic controllers create the required motion commands based on the platform application, deep learning approaches generate the required intelligence, and finally reinforcement learning techniques incrementally improve the generated intelligence until the mission is accomplished by the robot.