Layered controller synthesis for dynamic multi-agent systems

• URL: http://arxiv.org/abs/2307.06758v1
• Date: Thu, 13 Jul 2023 13:56:27 GMT
• Title: Layered controller synthesis for dynamic multi-agent systems
• Authors: Emily Clement, Nicolas Perrin-Gilbert, Philipp Schlehuber-Caissier
• Abstract summary: We present a layered approach for multi-agent control problem, decomposed into three stages. We use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one. First, a high-level plan for a coarse abstraction of the system is computed, relying on parametric timed automata augmented with stopwatches as they allow to efficiently model simplified dynamics of such systems. In the second stage, the high-level plan, based on SMT-formulation, mainly handles the combinatorial aspects of the problem, provides a more dynamically accurate solution. These stages are collectively referred to as the SWA-SMT solver. They are correct by construction but lack a crucial feature: they cannot be executed in real time. To overcome this, we use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy. We use reinforcement learning to train the policy, and show that the initial dataset is crucial for the overall success of the method.

Related papers

• Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems [11.012092202226855]
  Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment. This paper explores the possibility of using text-based models, i.e., large language models (LLMs), and text-and-image-based models (VLMs), as high-level planners for deadlock resolution. We propose a hierarchical control framework where a foundation model-based high-level planner helps to resolve deadlocks by assigning a leader to the MRS along with a set of waypoints for the MRS leader.
  arXiv  Detail & Related papers  (2024-04-09T16:03:26Z)
• Analyzing and Improving the Training Dynamics of Diffusion Models [36.37845647984578]
  We identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture. We find that systematic application of this philosophy eliminates the observed drifts and imbalances, resulting in considerably better networks at equal computational complexity.
  arXiv  Detail & Related papers  (2023-12-05T11:55:47Z)
• Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks [11.614036749291216]
  Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents. A key challenge within these approaches is that they often lack safety guarantees or the provided guarantees are impractical. This paper aims to address this challenge by checking if there exists a temporal composition of the trained NN controllers.
  arXiv  Detail & Related papers  (2023-11-17T20:51:24Z)
• Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt [103.58323875748427]
  This work offers a novel unsupervised pre-training solution for low-data regimes. Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models. Experimental results show that our method significantly boosts several QEIS models on three datasets.
  arXiv  Detail & Related papers  (2023-02-02T15:49:03Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Meta-Reinforcement Learning for Adaptive Control of Second Order Systems [3.131740922192114]
  In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. We formulate a meta reinforcement learning (meta-RL) control strategy that takes advantage of known, offline information for training, such as a model structure. A key design element is the ability to leverage model-based information offline during training, while maintaining a model-free policy structure for interacting with new environments.
  arXiv  Detail & Related papers  (2022-09-19T18:51:33Z)
• Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation [118.27432851053335]
  This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track. The No Interaction track targets for learning policies from pre-collected demonstration trajectories. In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms.
  arXiv  Detail & Related papers  (2022-06-13T16:20:42Z)
• Controllable Dynamic Multi-Task Architectures [92.74372912009127]
  We propose a controllable multi-task network that dynamically adjusts its architecture and weights to match the desired task preference as well as the resource constraints. We propose a disentangled training of two hypernetworks, by exploiting task affinity and a novel branching regularized loss, to take input preferences and accordingly predict tree-structured models with adapted weights.
  arXiv  Detail & Related papers  (2022-03-28T17:56:40Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line [0.0]
  The proposed approach combines mathematical modeling as a base algorithm and a cooperative Multi-Agent Reinforcement Learning system. We demonstrate how Deep Q-Learning can be applied to a real-life task in a heavy industry, resulting in significant improvement of previously existing automation systems.
  arXiv  Detail & Related papers  (2020-08-16T15:10:39Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

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.