# Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2305.19746 · HTML: https://mlsanity.com/paper/2305.19746/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 11 of 11)

- [Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans](https://mlsanity.com/paper/2310.19427/) (2023)
- [State-Covering Trajectory Stitching for Diffusion Planners](https://mlsanity.com/paper/2506.00895/) (2025)
- [Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning](https://mlsanity.com/paper/2506.00867/) (2025)
- [Policy Compatible Skill Incremental Learning via Lazy Learning Interface](https://mlsanity.com/paper/2509.20612/) (2025)
- [Demonstration-Enhanced Adaptable Multi-Objective Robot Navigation](https://mlsanity.com/paper/2404.04857/) (2024)
- [Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/2405.11669/) (2024)
- [Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution](https://mlsanity.com/paper/2605.02167/) (2026)
- [Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution](https://mlsanity.com/paper/2606.22314/) (2026)
- [Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles](https://mlsanity.com/paper/2503.01127/) (2025)
- [Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Driving](https://mlsanity.com/paper/2506.23771/) (2025)
- [Learning to Navigate with Minimal Parameters: Decomposing Visual Navigation Through Closed-Form Geometric Interfaces](https://mlsanity.com/paper/2607.11029/) (2026)

## Abstract (arXiv)

For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to decide the most suitable navigation policy. However, most existing deep reinforcement learning based navigation policies are trained with a hand-engineered curriculum and reward function which are difficult to be deployed in a wide range of real-world scenarios. In this paper, we propose a framework to learn a family of low-level navigation policies and a high-level policy for deploying them. The main idea is that, instead of learning a single navigation policy with a fixed reward function, we simultaneously learn a family of policies that exhibit different behaviors with a wide range of reward functions. We then train the high-level policy which adaptively deploys the most suitable navigation skill. We evaluate our approach in simulation and the real world and demonstrate that our method can learn diverse navigation skills and adaptively deploy them. We also illustrate that our proposed hierarchical learning framework presents explainability by providing semantics for the behavior of an autonomous agent.