148 research outputs found
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
Consensus of Multi-agent Reinforcement Learning Systems: The Effect of Immediate Rewards
This paper studies the consensus problem of a leaderless, homogeneous, multi-agent reinforcement learning (MARL) system using actor-critic algorithms with and without malicious agents. The goal of each agent is to reach the consensus position with the maximum cumulative reward. Although the reward function converges in both scenarios, in the absence of the malicious agent, the cumulative reward is higher than with the malicious agent present. We consider here various immediate reward functions. First, we study the immediate reward function based on Manhattan distance. In addition to proposing three different immediate reward functions based on Euclidean, -norm, and Chebyshev distances, we have rigorously shown which method has a better performance based on a cumulative reward for each agent and the entire team of agents. Finally, we present a combination of various immediate reward functions that yields a higher cumulative reward for each agent and the team of agents. By increasing the agents’ cumulative reward using the combined immediate reward function, we have demonstrated that the cumulative team reward in the presence of a malicious agent is comparable with the cumulative team reward in the absence of the malicious agent. The claims have been proven theoretically, and the simulation confirms theoretical findings
Cooperative optimal preview tracking for linear descriptor multi-agent systems
© 2018 The Franklin Institute. In this paper, a cooperative optimal preview tracking problem is considered for continuous-time descriptor multi-agent systems with a directed topology containing a spanning tree. By the acyclic assumption and state augmentation technique, it is shown that the cooperative tracking problem is equivalent to local optimal regulation problems of a set of low-dimensional descriptor augmented subsystems. To design distributed optimal preview controllers, restricted system equivalent (r.s.e.) and preview control theory are first exploited to obtain optimal preview controllers for reduced-order normal subsystems. Then, by using the invertibility of restricted equivalent relations, a constructive method for designing distributed controller is presented which also yields an explicit admissible solution for the generalized algebraic Riccati equation. Sufficient conditions for achieving global cooperative preview tracking are proposed proving that the distributed controllers are able to stabilize the descriptor augmented subsystems asymptotically. Finally, the validity of the theoretical results is illustrated via numerical simulation
Data-Driven Integral Reinforcement Learning for Continuous-Time Non-Zero-Sum Games
This paper develops an integral value iteration (VI) method to efficiently find online the Nash equilibrium solution of two-player non-zero-sum (NZS) differential games for linear systems with partially unknown dynamics. To guarantee the closed-loop stability about the Nash equilibrium, the explicit upper bound for the discounted factor is given. To show the efficacy of the presented online model-free solution, the integral VI method is compared with the model-based off-line policy iteration method. Moreover, the theoretical analysis of the integral VI algorithm in terms of three aspects, i.e., positive definiteness properties of the updated cost functions, the stability of the closed-loop systems, and the conditions that guarantee the monotone convergence, is provided in detail. Finally, the simulation results demonstrate the efficacy of the presented algorithms
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
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