Towards a Better Understanding of Learning with Multiagent Teams
- URL: http://arxiv.org/abs/2306.16205v1
- Date: Wed, 28 Jun 2023 13:37:48 GMT
- Title: Towards a Better Understanding of Learning with Multiagent Teams
- Authors: David Radke, Kate Larson, Tim Brecht and Kyle Tilbury
- Abstract summary: We show that some team structures help agents learn to specialize into specific roles, resulting in more favorable global results.
Large teams create credit assignment challenges that reduce coordination, leading to large teams performing poorly compared to smaller ones.
- Score: 4.746424588605832
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: While it has long been recognized that a team of individual learning agents
can be greater than the sum of its parts, recent work has shown that larger
teams are not necessarily more effective than smaller ones. In this paper, we
study why and under which conditions certain team structures promote effective
learning for a population of individual learning agents. We show that,
depending on the environment, some team structures help agents learn to
specialize into specific roles, resulting in more favorable global results.
However, large teams create credit assignment challenges that reduce
coordination, leading to large teams performing poorly compared to smaller
ones. We support our conclusions with both theoretical analysis and empirical
results.
Related papers
- Learning to Learn Group Alignment: A Self-Tuning Credo Framework with
Multiagent Teams [1.370633147306388]
Mixed incentives among a population with multiagent teams has been shown to have advantages over a fully cooperative system.
We propose a framework where individual learning agents self-regulate their configuration of incentives through various parts of their reward function.
arXiv Detail & Related papers (2023-04-14T18:16:19Z) - Informational Diversity and Affinity Bias in Team Growth Dynamics [6.729250803621849]
We show that the benefits of informational diversity are in tension with affinity bias.
Our results formalize a fundamental limitation of utility-based motivations to drive informational diversity.
arXiv Detail & Related papers (2023-01-28T05:02:40Z) - Learning to Transfer Role Assignment Across Team Sizes [48.43860606706273]
We propose a framework to learn role assignment and transfer across team sizes.
We demonstrate that re-using the role-based credit assignment structure can foster the learning process of larger reinforcement learning teams.
arXiv Detail & Related papers (2022-04-17T11:22:01Z) - Flat Teams Drive Scientific Innovation [43.65818554474622]
We show how individual activities cohere into broad roles of leadership through the direction and presentation of research.
The hidden hierarchy of a scientific team is characterized by its lead (or L)-ratio of members playing leadership roles to total team size.
We find that relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas.
arXiv Detail & Related papers (2022-01-18T04:07:49Z) - Team Power and Hierarchy: Understanding Team Success [11.09080707714613]
This research examines in depth the relationships between team power and team success in the field of Computer Science.
By analyzing 4,106,995 CS teams, we find that high power teams with flat structure have the best performance.
On the contrary, low-power teams with hierarchical structure is a facilitator of team performance.
arXiv Detail & Related papers (2021-08-09T15:10:58Z) - Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team
Composition [88.26752130107259]
In real-world multiagent systems, agents with different capabilities may join or leave without altering the team's overarching goals.
We propose COPA, a coach-player framework to tackle this problem.
We 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
arXiv Detail & Related papers (2021-05-18T17:27:37Z) - Learning to Incentivize Other Learning Agents [73.03133692589532]
We show how to equip RL agents with the ability to give rewards directly to other agents, using a learned incentive function.
Such agents significantly outperform standard RL and opponent-shaping agents in challenging general-sum Markov games.
Our work points toward more opportunities and challenges along the path to ensure the common good in a multi-agent future.
arXiv Detail & Related papers (2020-06-10T20:12:38Z) - Learning from Learners: Adapting Reinforcement Learning Agents to be
Competitive in a Card Game [71.24825724518847]
We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game.
We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
arXiv Detail & Related papers (2020-04-08T14:11:05Z) - On Emergent Communication in Competitive Multi-Agent Teams [116.95067289206919]
We investigate whether competition for performance from an external, similar agent team could act as a social influence.
Our results show that an external competitive influence leads to improved accuracy and generalization, as well as faster emergence of communicative languages.
arXiv Detail & Related papers (2020-03-04T01:14:27Z)
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.