Quantifying environment and population diversity in multi-agent reinforcement learning

• URL: http://arxiv.org/abs/2102.08370v1
• Date: Tue, 16 Feb 2021 18:54:39 GMT
• Title: Quantifying environment and population diversity in multi-agent reinforcement learning
• Authors: Kevin R. McKee and Joel Z. Leibo and Charlie Beattie and Richard Everett
• Abstract summary: Generalization is a major challenge for multi-agent reinforcement learning. In this paper, we investigate and quantify the relationship between generalization and diversity in the multi-agent domain. To better understand the effects of co-player variation, our experiments introduce a new environment-agnostic measure of behavioral diversity.
• Score: 7.548322030720646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players? In this paper, we investigate and quantify the relationship between generalization and diversity in the multi-agent domain. Across the range of multi-agent environments considered here, procedurally generating training levels significantly improves agent performance on held-out levels. However, agent performance on the specific levels used in training sometimes declines as a result. To better understand the effects of co-player variation, our experiments introduce a new environment-agnostic measure of behavioral diversity. Results demonstrate that population size and intrinsic motivation are both effective methods of generating greater population diversity. In turn, training with a diverse set of co-players strengthens agent performance in some (but not all) cases.

Related papers

• SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning [58.84311336011451]
  We propose a novel gradient-based state representation for multi-agent reinforcement learning. We employ denoising score matching to learn the social gradient fields (SocialGFs) from offline samples. In practice, we integrate SocialGFs into the widely used multi-agent reinforcement learning algorithms, e.g., MAPPO.
  arXiv  Detail & Related papers  (2024-05-03T04:12:19Z)
• Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization [63.554226552130054]
  Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL) The extent to which an agent is influenced by unseen co-players depends on the agent's policy and the specific scenario. We present the Level of Influence (LoI), a metric quantifying the interaction intensity among agents within a given scenario and environment.
  arXiv  Detail & Related papers  (2023-10-11T06:09:26Z)
• 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)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• Multi-Agent Interplay in a Competitive Survival Environment [0.0]
  This thesis is part of the author's thesis "Multi-Agent Interplay in a Competitive Survival Environment" for the Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome, 2022.
  arXiv  Detail & Related papers  (2023-01-19T12:04:03Z)
• ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward [29.737986509769808]
  We propose a self-supervised intrinsic reward ELIGN - expectation alignment. Similar to how animals collaborate in a decentralized manner with those in their vicinity, agents trained with expectation alignment learn behaviors that match their neighbors' expectations. We show that agent coordination improves through expectation alignment because agents learn to divide tasks amongst themselves, break coordination symmetries, and confuse adversaries.
  arXiv  Detail & Related papers  (2022-10-09T22:24:44Z)
• Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity [49.68758494467258]
  We study how to construct diverse populations of agents by carefully structuring how individuals within a population interact. Our approach is based on interaction graphs, which control the flow of information between agents during training. We provide evidence for the importance of diversity in multi-agent training and analyse the effect of applying different interaction graphs on the training trajectories, diversity and performance of populations in a range of games.
  arXiv  Detail & Related papers  (2021-10-08T11:29:52Z)
• 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)
• Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning [0.2538209532048867]
  This paper proposes a novel knowledge transfer framework in MARL, PAT (Parallel Attentional Transfer) We design two acting modes in PAT, student mode and self-learning mode. When agents are unfamiliar with the environment, the shared attention mechanism in student mode effectively selects learning knowledge from other agents to decide agents' actions.
  arXiv  Detail & Related papers  (2020-03-29T17:42:00Z)
• Effective Diversity in Population Based Reinforcement Learning [38.62641968788987]
  We introduce an approach to optimize all members of a population simultaneously. Rather than using pairwise distance, we measure the volume of the entire population in a behavioral manifold. Our algorithm Diversity via Determinants (DvD) adapts the degree of diversity during training using online learning techniques.
  arXiv  Detail & Related papers  (2020-02-03T10:09:16Z)

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.