# Deep Reinforcement Learning for Robotic Manipulation-The state of the art

arXiv: https://arxiv.org/abs/1701.08878 · HTML: https://mlsanity.com/paper/1701.08878/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 7 of 7)

- [ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03081/) (2023)
- [Machine Learning Meets Advanced Robotic Manipulation](https://mlsanity.com/paper/2309.12560/) (2023)
- [State-based Episodic Memory for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.09817/) (2021)
- [Adapting Image-based RL Policies via Predicted Rewards](https://mlsanity.com/paper/2407.16842/) (2024)
- [HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation](https://mlsanity.com/paper/2501.12703/) (2025)
- [A Versatile Agent for Fast Learning from Human Instructors](https://mlsanity.com/paper/2203.00251/) (2022)
- [Object Manipulation in Marine Environments using Reinforcement Learning](https://mlsanity.com/paper/2406.03223/) (2024)

## Abstract (arXiv)

The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.