Networked Agents in the Dark: Team Value Learning under Partial Observability
- URL: http://arxiv.org/abs/2501.08778v1
- Date: Wed, 15 Jan 2025 13:01:32 GMT
- Title: Networked Agents in the Dark: Team Value Learning under Partial Observability
- Authors: Guilherme S. Varela, Alberto Sardinha, Francisco S. Melo,
- Abstract summary: We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents.
In contrast to previous methods that rely on complete state information or joint observations, our agents must learn how to reach shared objectives under partial observability.
During training, they collect individual rewards and approximate a team value function through local communication, resulting in cooperative behavior.
- Score: 3.8779763612314633
- License:
- Abstract: We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents. In contrast to previous methods that rely on complete state information or joint observations, our agents must learn how to reach shared objectives under partial observability. During training, they collect individual rewards and approximate a team value function through local communication, resulting in cooperative behavior. To describe our problem, we introduce the networked dynamic partially observable Markov game framework, where agents communicate over a switching topology communication network. Our distributed method, DNA-MARL, uses a consensus mechanism for local communication and gradient descent for local computation. DNA-MARL increases the range of the possible applications of networked agents, being well-suited for real world domains that impose privacy and where the messages may not reach their recipients. We evaluate DNA-MARL across benchmark MARL scenarios. Our results highlight the superior performance of DNA-MARL over previous methods.
Related papers
- Collaborative Information Dissemination with Graph-based Multi-Agent
Reinforcement Learning [2.9904113489777826]
This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach for efficient information dissemination.
We propose a Partially Observable Game (POSG) for information dissemination empowering each agent to decide on message forwarding independently.
Our experimental results show that our trained policies outperform existing methods.
arXiv Detail & Related papers (2023-08-25T21:30:16Z) - 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) - Efficient Communication via Self-supervised Information Aggregation for
Online and Offline Multi-agent Reinforcement Learning [12.334522644561591]
We argue that efficient message aggregation is essential for good coordination in cooperative Multi-Agent Reinforcement Learning (MARL)
We propose Multi-Agent communication via Self-supervised Information Aggregation (MASIA), where agents can aggregate the received messages into compact representations with high relevance to augment the local policy.
We build offline benchmarks for multi-agent communication, which is the first as we know.
arXiv Detail & Related papers (2023-02-19T16:02:16Z) - Centralized Training with Hybrid Execution in Multi-Agent Reinforcement
Learning [7.163485179361718]
We introduce hybrid execution in multi-agent reinforcement learning (MARL)
MARL is a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time.
We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations.
arXiv Detail & Related papers (2022-10-12T14:58:32Z) - FCMNet: Full Communication Memory Net for Team-Level Cooperation in
Multi-Agent Systems [15.631744703803806]
We introduce FCMNet, a reinforcement learning based approach that allows agents to simultaneously learn an effective multi-hop communications protocol.
Using a simple multi-hop topology, we endow each agent with the ability to receive information sequentially encoded by every other agent at each time step.
FCMNet outperforms state-of-the-art communication-based reinforcement learning methods in all StarCraft II micromanagement tasks.
arXiv Detail & Related papers (2022-01-28T09:12:01Z) - Locality Matters: A Scalable Value Decomposition Approach for
Cooperative Multi-Agent Reinforcement Learning [52.7873574425376]
Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents.
We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Training Decentralized Execution paradigm.
arXiv Detail & Related papers (2021-09-22T10:08:15Z) - Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN)
Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot.
We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
arXiv Detail & Related papers (2021-03-08T21:48:55Z) - Cooperative Policy Learning with Pre-trained Heterogeneous Observation
Representations [51.8796674904734]
We propose a new cooperative learning framework with pre-trained heterogeneous observation representations.
We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
arXiv Detail & Related papers (2020-12-24T04:52:29Z) - Dif-MAML: Decentralized Multi-Agent Meta-Learning [54.39661018886268]
We propose a cooperative multi-agent meta-learning algorithm, referred to as MAML or Dif-MAML.
We show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML.
Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
arXiv Detail & Related papers (2020-10-06T16:51:09Z) - Networked Multi-Agent Reinforcement Learning with Emergent Communication [18.47483427884452]
Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents.
One way to coordinate is by learning to communicate with each other.
Can the agents develop a language while learning to perform a common task?
arXiv Detail & Related papers (2020-04-06T16:13:23Z) - Learning Structured Communication for Multi-agent Reinforcement Learning [104.64584573546524]
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
We propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology.
arXiv Detail & Related papers (2020-02-11T07:19:45Z)
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.