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

2022-09-17 · Yunbo Qiu, Yuzhu Zhan, Yue Jin, Jian Wang, Xudong Zhang

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.

📄 PDF Abstract BibTeX arXiv:2209.08351

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control

2022-09-17 · Yunbo Qiu, Yue Jin, Jian Wang, Xudong Zhang

Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions with obstacles and collisions among agents…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning

2022-09-10 · Yunxiao Guo, Xinjia Xie, Runhao Zhao, Chenglan Zhu 외

Flocking is a very challenging problem in a multi-agent system; traditional flocking methods also require complete knowledge of the environment and a precise model for control. In this paper, we propose Evolutionary Mult…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning

2019-05-10 · Carsten Hahn, Thomy Phan, Thomas Gabor, Lenz Belzner 외

In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we propose SELFish (Swarm Emergent Learning Fi…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Challenges Faced by Large Language Models in Solving Multi-Agent Flocking

2024-04-06 · Peihan Li, Vishnu Menon, Bhavanaraj Gudiguntla, Daniel Ting 외

Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the natural world and has applications in rob…

Decision Making Spatial Reasoning

Flexible Distributed Flocking Control for Multi-agent Unicycle Systems

2023-08-08 · Tinghua Li, Bayu Jayawardhana

Currently, the general aim of flocking and formation control laws for multi-agent systems is to form and maintain a rigid configuration, such as, the alpha-lattices in flocking control methods, where the desired distance…