# Multi-Agent Deep Reinforcement Learning with Human Strategies

arXiv: https://arxiv.org/abs/1806.04562 · HTML: https://mlsanity.com/paper/1806.04562/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

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

- [RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT](https://mlsanity.com/paper/2212.14647/) (2022)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.01288/) (2022)
- [Aligning Large Vision-Language Models by Deep Reinforcement Learning and Direct Preference Optimization](https://mlsanity.com/paper/2509.06759/) (2025)
- [Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks](https://mlsanity.com/paper/2404.05840/) (2024)

## Abstract (arXiv)

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multi-agent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms.