# Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning

arXiv: https://arxiv.org/abs/2302.13025 · HTML: https://mlsanity.com/paper/2302.13025/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity](https://mlsanity.com/paper/2305.04180/) (2023)
- [SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning](https://mlsanity.com/paper/2506.04505/) (2025)
- [SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas](https://mlsanity.com/paper/2510.19766/) (2025)
- [CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration](https://mlsanity.com/paper/2503.14254/) (2025)
- [ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments](https://mlsanity.com/paper/2502.19892/) (2025)

## Abstract (arXiv)

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://github.com/BeamanLi/CCRL_Exploration.