CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2208.13626v1
• Date: Fri, 26 Aug 2022 02:21:31 GMT
• Title: CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
• Authors: Vasu Sharma, Prasoon Goyal, Kaixiang Lin, Govind Thattai, Qiaozi Gao, Gaurav S. Sukhatme
• Abstract summary: We introduce a benchmark dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol.
• Score: 15.686200550604815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol. Our experiments investigate the impact of different modalities on multi-agent learning performance. We also introduce a simple message passing method between agents. The results suggest that multimodality introduces unique challenges for cooperative multi-agent learning and there is significant room for advancing multi-agent reinforcement learning methods in such settings.

Related papers

• Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation [0.7366405857677227]
  Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents. We introduce a novel framework that enables transfer learning for MARL through unifying various state spaces into fixed-size inputs. We show significant enhancements in multi-agent learning performance using maneuvering skills learned from other scenarios compared to agents learning from scratch.
  arXiv  Detail & Related papers  (2024-02-13T02:48:18Z)
• Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning [13.060023718506917]
  imitation learning (IL) is a problem of learning to mimic expert behaviors from demonstrations in cooperative multi-agent systems. We introduce a novel multi-agent IL algorithm designed to address these challenges. Our approach enables the centralized learning by leveraging mixing networks to aggregate decentralized Q functions.
  arXiv  Detail & Related papers  (2023-10-10T17:11:20Z)
• Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications [90.6849884683226]
  We study the challenge of interaction quantification in a semi-supervised setting with only labeled unimodal data. Using a precise information-theoretic definition of interactions, our key contribution is the derivation of lower and upper bounds. We show how these theoretical results can be used to estimate multimodal model performance, guide data collection, and select appropriate multimodal models for various tasks.
  arXiv  Detail & Related papers  (2023-06-07T15:44:53Z)
• UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning [29.237813880311943]
  We propose a novel multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. Experimental results with fused features on two image-text classification benchmarks show that our proposed Unimodality-Supervised MultiModal Contrastive UniS-MMC learning method outperforms current state-of-the-art multimodal methods.
  arXiv  Detail & Related papers  (2023-05-16T09:18:38Z)
• Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
  This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
  arXiv  Detail & Related papers  (2023-03-24T15:12:28Z)
• Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos [58.93586436289648]
  We propose a multi-scale cooperative multimodal transformer (MCMulT) architecture for multimodal sentiment analysis. Our model outperforms existing approaches on unaligned multimodal sequences and has strong performance on aligned multimodal sequences.
  arXiv  Detail & Related papers  (2022-06-16T07:47:57Z)
• Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL [107.58821842920393]
  We quantify the agent's behavior difference and build its relationship with the policy performance via bf Role Diversity We find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity. The decomposed factors can significantly impact policy optimization on three popular directions.
  arXiv  Detail & Related papers  (2022-06-01T04:58:52Z)
• Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction [125.18248926508045]
  We propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. CEN dynamically exchanges channels betweenworks of different modalities. For the application of dense image prediction, the validity of CEN is tested by four different scenarios.
  arXiv  Detail & Related papers  (2021-12-04T05:47:54Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)
• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning [20.901606233349177]
  Deep multi-agent reinforcement learning has shown the promise to solve complex cooperative tasks. In this paper, we aim to introduce diversity in both optimization and representation of shared multi-agent reinforcement learning. Our method achieves state-of-the-art performance on Google Research Football and super hard StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-06-04T00:55:03Z)

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.