# Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations

arXiv: https://arxiv.org/abs/1805.07095 · HTML: https://mlsanity.com/paper/1805.07095/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 30 of 38)

- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving](https://mlsanity.com/paper/2103.10690/) (2021)
- [Verifying Learning-Based Robotic Navigation Systems](https://mlsanity.com/paper/2205.13536/) (2022)
- [On Computation and Generalization of Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2001.02792/) (2020)
- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [Constrained Reinforcement Learning for Robotics via Scenario-Based Programming](https://mlsanity.com/paper/2206.09603/) (2022)
- [IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning](https://mlsanity.com/paper/2103.11686/) (2021)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [A Visual Navigation Perspective for Category-Level Object Pose Estimation](https://mlsanity.com/paper/2203.13572/) (2022)
- [Pre-trained Transformer-Enabled Strategies with Human-Guided Fine-Tuning for End-to-end Navigation of Autonomous Vehicles](https://mlsanity.com/paper/2402.12666/) (2024)
- [Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning](https://mlsanity.com/paper/2110.05286/) (2021)
- [Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control](https://mlsanity.com/paper/2210.01525/) (2022)
- [Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning](https://mlsanity.com/paper/2210.03729/) (2022)
- [Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control](https://mlsanity.com/paper/2112.06018/) (2021)
- [Data-Efficient Learning from Human Interventions for Mobile Robots](https://mlsanity.com/paper/2503.04969/) (2025)
- [Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations](https://mlsanity.com/paper/2509.05311/) (2025)
- [Risk Conditioned Neural Motion Planning](https://mlsanity.com/paper/2108.01851/) (2021)
- [Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study](https://mlsanity.com/paper/2203.14741/) (2022)
- [Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning](https://mlsanity.com/paper/2405.09760/) (2024)
- [Towards robust and domain agnostic reinforcement learning competitions](https://mlsanity.com/paper/2106.03748/) (2021)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model](https://mlsanity.com/paper/2108.06161/) (2021)
- [SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning](https://mlsanity.com/paper/2506.04505/) (2025)
- [A Comparative Evaluation of Teacher-Guided Reinforcement Learning Techniques for Autonomous Cyber Operations](https://mlsanity.com/paper/2508.14340/) (2025)
- [Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions](https://mlsanity.com/paper/2101.03525/) (2021)
- [Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach](https://mlsanity.com/paper/2109.08536/) (2021)
- [Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation](https://mlsanity.com/paper/2503.01146/) (2025)

## Abstract (arXiv)

This work presents a case study of a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer from a large sample complexity and the distribution mismatch problem, respectively, we show that leveraging prior expert demonstrations for pre-training can reduce the training time to reach at least the same level of performance compared to plain RL by a factor of 5. We present a thorough evaluation of different combinations of expert demonstrations, different RL algorithms and reward functions, both in simulation and on a real robotic platform. Our results show that the final model outperforms both standalone approaches in the amount of successful navigation tasks. In addition, the RL reward function can be significantly simplified when using pre-training, e.g. by using a sparse reward only. The learned navigation policy is able to generalize to unseen and real-world environments.