# A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

arXiv: https://arxiv.org/abs/1612.07139 · HTML: https://mlsanity.com/paper/1612.07139/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 6 of 6)

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- [Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning](https://mlsanity.com/paper/2112.00115/) (2021)
- [Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories](https://mlsanity.com/paper/2302.08669/) (2023)
- [LARG, Language-based Automatic Reward and Goal Generation](https://mlsanity.com/paper/2306.10985/) (2023)
- [Robust Behavior Cloning Via Global Lipschitz Regularization](https://mlsanity.com/paper/2506.19250/) (2025)

## Abstract (arXiv)

Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.