# Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control

arXiv: https://arxiv.org/abs/2209.08351 · HTML: https://mlsanity.com/paper/2209.08351/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Multi-Agent Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1807.09936/) (2018)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments](https://mlsanity.com/paper/1910.04281/) (2019)
- [Multi-vehicle Flocking Control with Deep Deterministic Policy Gradient Method](https://mlsanity.com/paper/1806.00196/) (2018)

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

- [Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2403.08936/) (2024)
- [Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.02890/) (2024)

## Abstract (arXiv)

Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the cooperativity and safety of agents. In contrast to traditional methods, multi-agent reinforcement learning (MARL) solves the problem of flocking control more flexibly. However, methods based on MARL suffer from sample inefficiency, since they require a huge number of experiences to be collected from interactions between agents and the environment. We propose a novel method Pretraining with Demonstrations for MARL (PwD-MARL), which can utilize non-expert demonstrations collected in advance with traditional methods to pretrain agents. During the process of pretraining, agents learn policies from demonstrations by MARL and behavior cloning simultaneously, and are prevented from overfitting demonstrations. By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start. Experiments show that PwD-MARL improves sample efficiency and policy performance in the problem of flocking control, even with bad or few demonstrations.