A Regret Minimization Approach to Multi-Agent Control

• URL: http://arxiv.org/abs/2201.13288v2
• Date: Tue, 1 Feb 2022 02:22:22 GMT
• Title: A Regret Minimization Approach to Multi-Agent Control
• Authors: Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan
• Abstract summary: We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances. We give a reduction from any (standard) regret minimizing control method to a distributed algorithm. We show that the distributed method is robust to failure and to adversarial perturbations in the dynamics.
• Score: 24.20403443262127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances. Our study focuses on optimal control without centralized precomputed policies, but rather with adaptive control policies for the different agents that are only equipped with a stabilizing controller. We give a reduction from any (standard) regret minimizing control method to a distributed algorithm. The reduction guarantees that the resulting distributed algorithm has low regret relative to the optimal precomputed joint policy. Our methodology involves generalizing online convex optimization to a multi-agent setting and applying recent tools from nonstochastic control derived for a single agent. We empirically evaluate our method on a model of an overactuated aircraft. We show that the distributed method is robust to failure and to adversarial perturbations in the dynamics.

Related papers

• Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression [3.405252606286664]
  This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. For continuous enhancement in predictive performance, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies.
  arXiv  Detail & Related papers  (2024-02-05T16:41:17Z)
• Introduction to Online Nonstochastic Control [34.77535508151501]
  In online nonstochastic control, both the cost functions as well as the perturbations from the assumed dynamical model are chosen by an adversary. The target is to attain low regret against the best policy in hindsight from a benchmark class of policies.
  arXiv  Detail & Related papers  (2022-11-17T16:12:45Z)
• Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning [9.939081691797858]
  We present a reinforcement learning (RL) approach for the multi-agent efficient domain coverage problem involving agents with second-order dynamics. Our proposed network architecture includes the incorporation of LSTM and self-attention, which allows the trained policy to adapt to a variable number of agents.
  arXiv  Detail & Related papers  (2022-11-11T01:59:12Z)
• Stabilizing Dynamical Systems via Policy Gradient Methods [32.88312419270879]
  We provide a model-free algorithm for stabilizing fully observed dynamical systems. We prove that this method efficiently recovers a stabilizing controller for linear systems. We empirically evaluate the effectiveness of our approach on common control benchmarks.
  arXiv  Detail & Related papers  (2021-10-13T00:58:57Z)
• Imitation Learning from MPC for Quadrupedal Multi-Gait Control [63.617157490920505]
  We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. We use and extend MPC-Net, which is an Imitation Learning approach guided by Model Predictive Control. We validate our approach on hardware and show that a single learned policy can replace its teacher to control multiple gaits.
  arXiv  Detail & Related papers  (2021-03-26T08:48:53Z)
• Improper Learning with Gradient-based Policy Optimization [62.50997487685586]
  We consider an improper reinforcement learning setting where the learner is given M base controllers for an unknown Markov Decision Process. We propose a gradient-based approach that operates over a class of improper mixtures of the controllers.
  arXiv  Detail & Related papers  (2021-02-16T14:53:55Z)
• Enforcing robust control guarantees within neural network policies [76.00287474159973]
  We propose a generic nonlinear control policy class, parameterized by neural networks, that enforces the same provable robustness criteria as robust control. We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods.
  arXiv  Detail & Related papers  (2020-11-16T17:14:59Z)
• Stein Variational Model Predictive Control [130.60527864489168]
  Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
  arXiv  Detail & Related papers  (2020-11-15T22:36:59Z)
• Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control [3.3788926259119645]
  In decentralized multi-agent control, systems are complex with unknown or highly uncertain dynamics. Deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the knowing system dynamics. Existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system. We propose a new MARL algorithm for decentralized multi-agent control with a stability guarantee.
  arXiv  Detail & Related papers  (2020-09-20T06:11:42Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

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.