Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics
- URL: http://arxiv.org/abs/2304.05872v2
- Date: Wed, 06 Nov 2024 21:31:13 GMT
- Title: Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics
- Authors: Philipp Dominic Siedler,
- Abstract summary: We propose a Graph Neural Network (GNN) based communication mechanism that increases the agents' observation space.
While the goal of the agent collective is to clean up as much as possible, agents are rewarded for the individual amount of macroplastics collected.
We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate.
- Score: 0.0
- License:
- Abstract: Finding a balance between collaboration and competition is crucial for artificial agents in many real-world applications. We investigate this using a Multi-Agent Reinforcement Learning (MARL) setup on the back of a high-impact problem. The accumulation and yearly growth of plastic in the ocean cause irreparable damage to many aspects of oceanic health and the marina system. To prevent further damage, we need to find ways to reduce macroplastics from known plastic patches in the ocean. Here we propose a Graph Neural Network (GNN) based communication mechanism that increases the agents' observation space. In our custom environment, agents control a plastic collecting vessel. The communication mechanism enables agents to develop a communication protocol using a binary signal. While the goal of the agent collective is to clean up as much as possible, agents are rewarded for the individual amount of macroplastics collected. Hence agents have to learn to communicate effectively while maintaining high individual performance. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show communication enables collaboration and increases collective performance significantly. This means agents have learned the importance of communication and found a balance between collaboration and competition.
Related papers
- Scaling Large-Language-Model-based Multi-Agent Collaboration [75.5241464256688]
Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration.
Inspired by the neural scaling law, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration.
arXiv Detail & Related papers (2024-06-11T11:02:04Z) - ProAgent: Building Proactive Cooperative Agents with Large Language
Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents.
ProAgent can analyze the present state, and infer the intentions of teammates from observations.
ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - Dynamic Collaborative Multi-Agent Reinforcement Learning Communication
for Autonomous Drone Reforestation [0.0]
We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup.
Agents can communicate as part of a dynamically changing network.
Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.
arXiv Detail & Related papers (2022-11-14T13:25:22Z) - Certifiably Robust Policy Learning against Adversarial Communication in
Multi-agent Systems [51.6210785955659]
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored.
In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $CfracN-12$ agents to a victim agent.
arXiv Detail & Related papers (2022-06-21T07:32:18Z) - PooL: Pheromone-inspired Communication Framework forLarge Scale
Multi-Agent Reinforcement Learning [0.0]
textbfPooL is an indirect communication framework applied to large scale multi-agent reinforcement textbfl.
PooL uses the release and utilization mechanism of pheromones to control large-scale agent coordination.
PooL can capture effective information and achieve higher rewards than other state-of-arts methods with lower communication costs.
arXiv Detail & Related papers (2022-02-20T03:09:53Z) - Interpretation of Emergent Communication in Heterogeneous Collaborative
Embodied Agents [83.52684405389445]
We introduce the collaborative multi-object navigation task CoMON.
In this task, an oracle agent has detailed environment information in the form of a map.
It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals.
We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
arXiv Detail & Related papers (2021-10-12T06:56:11Z) - Adversarial Attacks On Multi-Agent Communication [80.4392160849506]
Modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
Such advantages rely heavily on communication channels which have been shown to be vulnerable to security breaches.
In this paper, we explore such adversarial attacks in a novel multi-agent setting where agents communicate by sharing learned intermediate representations.
arXiv Detail & Related papers (2021-01-17T00:35:26Z) - The Emergence of Adversarial Communication in Multi-Agent Reinforcement
Learning [6.18778092044887]
Many real-world problems require the coordination of multiple autonomous agents.
Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination.
We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
arXiv Detail & Related papers (2020-08-06T12:48:08Z) - Learning Individually Inferred Communication for Multi-Agent Cooperation [37.56115000150748]
We propose Individually Inferred Communication (I2C) to enable agents to learn a prior for agent-agent communication.
The prior knowledge is learned via causal inference and realized by a feed-forward neural network.
I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios.
arXiv Detail & Related papers (2020-06-11T14:07:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.