# A Survey on Reinforcement Learning Applications in SLAM

arXiv: https://arxiv.org/abs/2408.14518 · HTML: https://mlsanity.com/paper/2408.14518/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 109 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion](https://mlsanity.com/paper/1807.01675/) (2018)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment](https://mlsanity.com/paper/2007.11808/) (2020)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [Particle Filter SLAM for Vehicle Localization](https://mlsanity.com/paper/2402.07429/) (2024)
- [Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion](https://mlsanity.com/paper/2404.06926/) (2024)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces](https://mlsanity.com/paper/1905.09700/) (2019)
- [Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning](https://mlsanity.com/paper/2110.00640/) (2021)
- [Radar SLAM: A Robust SLAM System for All Weather Conditions](https://mlsanity.com/paper/2104.05347/) (2021)
- [Active SLAM: A Review On Last Decade](https://mlsanity.com/paper/2212.11654/) (2022)
- [RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization](https://mlsanity.com/paper/2202.13221/) (2022)
- [Real-time Motion Planning for autonomous vehicles in dynamic environments](https://mlsanity.com/paper/2406.02916/) (2024)
- [Tightly-coupled Monocular Visual-odometric SLAM using Wheels and a MEMS Gyroscope](https://mlsanity.com/paper/1804.04854/) (2018)
- [Research on Robot Path Planning Based on Reinforcement Learning](https://mlsanity.com/paper/2404.14077/) (2024)

## Built on by (most cited first, 3 of 3)

- [RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation](https://mlsanity.com/paper/2505.14526/) (2025)
- [Attention-based U-Net Method for Autonomous Lane Detection](https://mlsanity.com/paper/2411.10902/) (2024)
- [Reliability-Aware Future Conditioning for Temporally Robust Robot Manipulation](https://mlsanity.com/paper/2610.11956/) (2026)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estimating its own pose within that map. Reinforcement learning (RL), in which an agent learns a decision policy from interaction and reward, has been applied to decide how such systems move, explore, and recognize places they have visited before. This survey reviews the applications of RL in SLAM. We first distinguish passive SLAM, in which the robot's motion is not chosen by the SLAM system, from active SLAM, in which it is, and summarize the sensors that provide the input to SLAM. We then introduce the RL methods used in this literature, from value-based and policy-based methods to actor-critic and deep RL. Next, we classify RL applications in SLAM into three categories: path planning, including environment exploration and obstacle avoidance; loop closure detection; and active SLAM. Thirteen representative studies are compared in terms of their simulation environment, deep learning method, SLAM method, and RL algorithm. Most of these studies are evaluated mainly in simulation, and value-based methods from the deep Q-network family are the most common. Finally, we discuss the challenges of applying RL to SLAM, namely computational demands, safety, generalization, high-dimensional state and action spaces, sample efficiency, and sensor and actuator delays, and we outline directions for future research.