Few-Shot Teamwork

• URL: http://arxiv.org/abs/2207.09300v1
• Date: Tue, 19 Jul 2022 14:34:41 GMT
• Title: Few-Shot Teamwork
• Authors: Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
• Abstract summary: We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task. We discuss how the FST problem can be seen as addressing two separate problems: one of reducing the experience required to train a team of agents to complete a complex task; and one of collaborating with unfamiliar teammates to complete a new task.
• Score: 6.940758395823777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task. We discuss how the FST problem can be seen as addressing two separate problems: one of reducing the experience required to train a team of agents to complete a complex task; and one of collaborating with unfamiliar teammates to complete a new task. Progress towards solving FST could lead to progress in both multi-agent reinforcement learning and ad hoc teamwork.

Related papers

• Adaptive In-conversation Team Building for Language Model Agents [33.03550687362213]
  Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks. Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
  arXiv  Detail & Related papers  (2024-05-29T18:08:37Z)
• Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts [75.75548749888029]
  We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks. With a single model, Musketeer achieves results comparable to or better than strong baselines trained on single tasks, almost uniformly across multiple tasks.
  arXiv  Detail & Related papers  (2023-05-11T17:57:49Z)
• Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition [11.998708550268978]
  We propose an approach which uses an expert-provided decomposition of a task into simpler multi-agent sub-tasks. In each sub-task, a subset of the entire team is trained to acquire sub-task-specific policies. The sub-teams are then merged and transferred to the target task, where their policies are collectively fine-tuned to solve the more complex target task.
  arXiv  Detail & Related papers  (2023-02-09T21:24:56Z)
• Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning [13.468555224407764]
  We show that a team of agents is able to adapt to novel tasks when provided with task embeddings. We propose three MATE training paradigms: independent MATE, centralised MATE, and mixed MATE. We show that the embeddings learned by MATE identify tasks and provide useful information which agents leverage during adaptation to novel tasks.
  arXiv  Detail & Related papers  (2022-07-05T18:23:20Z)
• 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)
• Multi-Agent Policy Transfer via Task Relationship Modeling [28.421365805638953]
  We try to discover and exploit common structures among tasks for more efficient transfer. We propose to learn effect-based task representations as a common space of tasks, using an alternatively fixed training scheme. As a result, the proposed method can help transfer learned cooperation knowledge to new tasks after training on a few source tasks.
  arXiv  Detail & Related papers  (2022-03-09T01:49:21Z)
• Towards Collaborative Question Answering: A Preliminary Study [63.91687114660126]
  We propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone. We make a synthetic dataset of a large knowledge graph that can be distributed to experts. We show that the problem can be challenging without introducing prior to the collaboration structure, unless experts are perfect and uniform.
  arXiv  Detail & Related papers  (2022-01-24T14:27:00Z)
• Environment Generation for Zero-Shot Compositional Reinforcement Learning [105.35258025210862]
  Compositional Design of Environments (CoDE) trains a Generator agent to automatically build a series of compositional tasks tailored to the agent's current skill level. We learn to generate environments composed of multiple pages or rooms, and train RL agents capable of completing wide-range of complex tasks in those environments. CoDE yields 4x higher success rate than the strongest baseline, and demonstrates strong performance of real websites learned on 3500 primitive tasks.
  arXiv  Detail & Related papers  (2022-01-21T21:35:01Z)
• Efficiently Identifying Task Groupings for Multi-Task Learning [55.80489920205404]
  Multi-task learning can leverage information learned by one task to benefit the training of other tasks. We suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
  arXiv  Detail & Related papers  (2021-09-10T02:01:43Z)
• On the Critical Role of Conventions in Adaptive Human-AI Collaboration [73.21967490610142]
  We propose a learning framework that teases apart rule-dependent representation from convention-dependent representation. We experimentally validate our approach on three collaborative tasks varying in complexity.
  arXiv  Detail & Related papers  (2021-04-07T02:46:19Z)
• Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward [0.0]
  We present Decomposed Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG), a novel cooperative multi-agent reinforcement learning framework. We show that our solution achieves a significantly better and more stable performance than the direct adaptation of the MADDPG algorithm.
  arXiv  Detail & Related papers  (2020-03-24T00:55:37Z)

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.