Parallel Best Arm Identification in Heterogeneous Environments

• URL: http://arxiv.org/abs/2207.08015v3
• Date: Thu, 18 Apr 2024 14:31:11 GMT
• Title: Parallel Best Arm Identification in Heterogeneous Environments
• Authors: Nikolai Karpov, Qin Zhang,
• Abstract summary: We study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model. By proving almost tight upper and lower bounds, we show that collaborative learning in the heterogeneous setting is inherently more difficult than that in the homogeneous setting.
• Score: 8.915120653822433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments and they want to learn in parallel an objective function in the aggregated environment. By proving almost tight upper and lower bounds, we show that collaborative learning in the heterogeneous setting is inherently more difficult than that in the homogeneous setting in terms of the time-round tradeoff.

Related papers

• POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation [76.67608003501479]
  We introduce and specify an evaluation protocol defining a range of domain-related metrics computed on the basics of the primary evaluation indicators. The results of such a comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.
  arXiv  Detail & Related papers  (2024-07-20T16:37:21Z)
• Collaboration in Immersive Environments: Challenges and Solutions [0.0]
  This paper provides an overview of the current state of research on collaboration in immersive environments. It discusses the different types of immersive environments, including VR and AR, and the different forms of collaboration that can occur in these environments.
  arXiv  Detail & Related papers  (2023-11-01T17:45:22Z)
• Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning [60.058083574671834]
  This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation. For heterogeneous issue, we leverage irrelevant unlabeled public data for communication. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation.
  arXiv  Detail & Related papers  (2023-09-28T09:32:27Z)
• Heterogeneous Embodied Multi-Agent Collaboration [21.364827833498254]
  Heterogeneous multi-agent tasks are common in real-world scenarios. We propose the heterogeneous multi-agent tidying-up task, in which multiple heterogeneous agents collaborate to detect misplaced objects and place them in reasonable locations. We propose the hierarchical decision model based on misplaced object detection, reasonable receptacle prediction, as well as the handshake-based group communication mechanism.
  arXiv  Detail & Related papers  (2023-07-26T04:33:05Z)
• Adaptive Coordination in Social Embodied Rearrangement [49.35582108902819]
  We study zero-shot coordination (ZSC) in this task, where an agent collaborates with a new partner, emulating a scenario where a robot collaborates with a new human partner. We propose Behavior Diversity Play (BDP), a novel ZSC approach that encourages diversity through a discriminability objective. Our results demonstrate that BDP learns adaptive agents that can tackle visual coordination, and zero-shot generalize to new partners in unseen environments, achieving 35% higher success and 32% higher efficiency compared to baselines.
  arXiv  Detail & Related papers  (2023-05-31T18:05:51Z)
• Rethinking Trajectory Prediction via "Team Game" [118.59480535826094]
  We present a novel formulation for multi-agent trajectory prediction, which explicitly introduces the concept of interactive group consensus. On two multi-agent settings, i.e. team sports and pedestrians, the proposed framework consistently achieves superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2022-10-17T07:16:44Z)
• Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning [71.53769213321202]
  We formalize the notions of coordination level and heterogeneity level of an environment. We present HECOGrid, a suite of multi-agent environments that facilitates empirical evaluation of different MARL approaches. We propose a Training Decentralized Execution learning approach that enables agents to work efficiently in high-coordination and high-heterogeneity environments.
  arXiv  Detail & Related papers  (2022-10-04T18:17:01Z)
• Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share? [7.489793155793319]
  This work focuses on combining information obtained through intrinsic motivation with the aim of having a more efficient exploration and faster learning. Our results reveal different ways in which a collaborative framework with little additional computational cost can outperform an independent learning process without knowledge sharing.
  arXiv  Detail & Related papers  (2022-02-24T16:15:51Z)
• 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)

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.