# Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path

arXiv: https://arxiv.org/abs/2003.01641 · HTML: https://mlsanity.com/paper/2003.01641/

## Key facts

- Cited by 5 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 3 Mar 2020.

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

- [MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints](https://mlsanity.com/paper/2101.06798/) (2021)
- [Model-free Neural Lyapunov Control for Safe Robot Navigation](https://mlsanity.com/paper/2203.01190/) (2022)
- [Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots](https://mlsanity.com/paper/2209.06328/) (2022)
- [Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization](https://mlsanity.com/paper/2209.06332/) (2022)
- [InfraVLA: Extending Vision-Language-Action Navigation with Infrastructure Cameras](https://mlsanity.com/paper/2609.33647/) (2026)

## Abstract (arXiv)

In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can efficiently learn to understand and generalize to different environments using high-dimensional inputs (a 2D map), while following feasible paths that avoid obstacles in obstacle-cluttered environment. To achieve this, we make use of traditional path planning algorithms, supervised learning, and reinforcement learning algorithms in a synergistic way. The key idea is to decouple the navigation problem into planning and control, the former of which is achieved by supervised learning whereas the latter is done by reinforcement learning. Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path. This allows the trained agent to achieve good generalization while learning faster. We test our proposed method in the recently proposed Safety Gym suite that allows testing of safety-constraints during training of learning agents. We compare our proposed method with existing work and show that our method consistently improves the sample efficiency and generalization capability to novel environments.