# Knowledge-Based Sequential Decision-Making Under Uncertainty

arXiv: https://arxiv.org/abs/1905.07030 · HTML: https://mlsanity.com/paper/1905.07030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 2 of 2)

- [Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation](https://mlsanity.com/paper/2305.11039/) (2023)
- [Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach](https://mlsanity.com/paper/2312.17284/) (2023)

## Abstract (arXiv)

Deep reinforcement learning (DRL) algorithms have achieved great success on sequential decision-making problems, yet is criticized for the lack of data-efficiency and explainability. Especially, explainability of subtasks is critical in hierarchical decision-making since it enhances the transparency of black-box-style DRL methods and helps the RL practitioners to understand the high-level behavior of the system better. To improve the data-efficiency and explainability of DRL, declarative knowledge is introduced in this work and a novel algorithm is proposed by integrating DRL with symbolic planning. Experimental analysis on publicly available benchmarks validates the explainability of the subtasks and shows that our method can outperform the state-of-the-art approach in terms of data-efficiency.