Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition

• URL: http://arxiv.org/abs/2302.04944v2
• Date: Thu, 15 Feb 2024 17:43:25 GMT
• Title: Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
• Authors: Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
• Abstract summary: 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.
• Score: 11.998708550268978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-stationarity caused by mutually adapting agents. To facilitate efficient learning of complex multi-agent tasks, 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. We show empirically that such approaches can greatly reduce the number of timesteps required to solve a complex target task relative to training from-scratch. However, we also identify and investigate two problems with naive implementations of approaches based on sub-task decomposition, and propose a simple and scalable method to address these problems which augments existing actor-critic algorithms. We demonstrate the empirical benefits of our proposed method, enabling sub-task decomposition approaches to be deployed in diverse multi-agent tasks.

Related papers

• Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing [33.915222518617085]
  This paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) It presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA) Experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods.
  arXiv  Detail & Related papers  (2024-10-20T17:00:45Z)
• Variational Offline Multi-agent Skill Discovery [43.869625428099425]
  We propose two novel auto-encoder schemes to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills. Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining.
  arXiv  Detail & Related papers  (2024-05-26T00:24:46Z)
• 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)
• Robust Subtask Learning for Compositional Generalization [20.54144051436337]
  We focus on the problem of training subtask policies in a way that they can be used to perform any task. We aim to maximize the worst-case performance over all tasks as opposed to the average-case performance.
  arXiv  Detail & Related papers  (2023-02-06T18:19:25Z)
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks [55.42850359286304]
  We propose Decomposed Prompting to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks. This modular structure allows each prompt to be optimized for its specific sub-task. We show that the flexibility and modularity of Decomposed Prompting allows it to outperform prior work on few-shot prompting.
  arXiv  Detail & Related papers  (2022-10-05T17:28:20Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division [60.232542918414985]
  Multi-task learning often suffers from negative transfer, sharing information that should be task-specific. This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared. We also demonstrate its ability to autonomously divide the tasks into both shared and task-specific sub-behaviours.
  arXiv  Detail & Related papers  (2022-03-28T15:53:17Z)
• Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
  We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
  arXiv  Detail & Related papers  (2021-09-15T21:19:11Z)
• 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)

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.