Search Results for author: Amanda Prorok

Found 30 papers, 17 papers with code

Constrained Environment Optimization for Prioritized Multi-Agent Navigation

no code implementations18 May 2023 Zhan Gao, Amanda Prorok

The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost are incorporated.

Stochastic Optimization

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

1 code implementation3 May 2023 Matteo Bettini, Ajay Shankar, Amanda Prorok

When a system of learning agents is not constrained to homogeneous policies, individual agents may develop diverse behaviors, resulting in emergent complementarity that benefits the system.

Multi-agent Reinforcement Learning

Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning

no code implementations24 Feb 2023 Ryan Kortvelesy, Steven Morad, Amanda Prorok

The problem of permutation-invariant learning over set representations is particularly relevant in the field of multi-agent systems -- a few potential applications include unsupervised training of aggregation functions in graph neural networks (GNNs), neural cellular automata on graphs, and prediction of scenes with multiple objects.

Heterogeneous Multi-Robot Reinforcement Learning

2 code implementations17 Jan 2023 Matteo Bettini, Ajay Shankar, Amanda Prorok

Through simulations and real-world experiments, we show that: (i) when homogeneous methods fail due to strong heterogeneous requirements, HetGPPO succeeds, and, (ii) when homogeneous methods are able to learn apparently heterogeneous behaviors, HetGPPO achieves higher resilience to both training and deployment noise.

Multi-agent Reinforcement Learning reinforcement-learning +1

Decentralized Channel Management in WLANs with Graph Neural Networks

no code implementations30 Oct 2022 Zhan Gao, Yulin Shao, Deniz Gunduz, Amanda Prorok

Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices.


Environment Optimization for Multi-Agent Navigation

no code implementations22 Sep 2022 Zhan Gao, Amanda Prorok

Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance.

See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation

no code implementations1 Aug 2022 Jan Blumenkamp, QingBiao Li, Binyu Wang, Zhe Liu, Amanda Prorok

We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person-view images.

Imitation Learning Navigate +1

QGNN: Value Function Factorisation with Graph Neural Networks

1 code implementation25 May 2022 Ryan Kortvelesy, Amanda Prorok

In multi-agent reinforcement learning, the use of a global objective is a powerful tool for incentivising cooperation.

Multi-agent Reinforcement Learning Starcraft

A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

2 code implementations2 Nov 2021 Jan Blumenkamp, Steven Morad, Jennifer Gielis, QingBiao Li, Amanda Prorok

We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication.

Graph Neural Network Guided Local Search for the Traveling Salesperson Problem

1 code implementation ICLR 2022 Benjamin Hudson, QingBiao Li, Matthew Malencia, Amanda Prorok

To close this gap, we present a hybrid data-driven approach for solving the TSP based on Graph Neural Networks (GNNs) and Guided Local Search (GLS).

Explanation-Aware Experience Replay in Rule-Dense Environments

1 code implementation29 Sep 2021 Francesco Sovrano, Alex Raymond, Amanda Prorok

In this paper, we propose a method for organising experience by means of partitioning the experience buffer into clusters labelled on a per-explanation basis.

Autonomous Driving Reinforcement Learning (RL)

Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

no code implementations25 Sep 2021 Amanda Prorok, Matthew Malencia, Luca Carlone, Gaurav S. Sukhatme, Brian M. Sadler, Vijay Kumar

In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts

no code implementations26 Jul 2021 Amanda Prorok, Jan Blumenkamp, QingBiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump

Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.

Graph Convolutional Memory using Topological Priors

1 code implementation27 Jun 2021 Steven D. Morad, Stephan Liwicki, Ryan Kortvelesy, Roberto Mecca, Amanda Prorok

Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the world.

Memorization reinforcement-learning +1

Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

1 code implementation18 May 2021 Lifeng Zhou, Vishnu D. Sharma, QingBiao Li, Amanda Prorok, Alejandro Ribeiro, Pratap Tokekar, Vijay Kumar

We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.

Decision Making Motion Planning

ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture

1 code implementation24 Mar 2021 Ryan Kortvelesy, Amanda Prorok

Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies.

Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning

no code implementations29 Dec 2020 Fernando Gama, QingBiao Li, Ekaterina Tolstaya, Amanda Prorok, Alejandro Ribeiro

Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information.

Imitation Learning

Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication

no code implementations1 Dec 2020 Rupert Mitchell, Jan Blumenkamp, Amanda Prorok

In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems.

Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning

1 code implementation26 Nov 2020 QingBiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok

Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.

Graph Attention

The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning

1 code implementation6 Aug 2020 Jan Blumenkamp, Amanda Prorok

Such a design choice, however, precludes the existence of a single, differentiable communication channel, and consequently prohibits the learning of inter-agent communication strategies.

Multi-agent Reinforcement Learning reinforcement-learning +1

Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

1 code implementation11 May 2020 Binyu Wang, Zhe Liu, Qing-Biao Li, Amanda Prorok

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects.

reinforcement-learning Reinforcement Learning (RL)

Graph Neural Networks for Decentralized Multi-Robot Path Planning

1 code implementation12 Dec 2019 Qing-Biao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok

We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations.

Decision Making

Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving

no code implementations26 Nov 2019 Rupert Mitchell, Jenny Fletcher, Jacopo Panerati, Amanda Prorok

In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed.

Autonomous Driving Mixed Reality +2

An Adversarial Approach to Private Flocking in Mobile Robot Teams

2 code implementations23 Sep 2019 Hehui Zheng, Jacopo Panerati, Giovanni Beltrame, Amanda Prorok

We present a method that generates private flocking controllers that hide the identity of the leader robot.

Motion Planning

Graph Neural Networks for Learning Robot Team Coordination

no code implementations9 May 2018 Amanda Prorok

This paper shows how Graph Neural Networks can be used for learning distributed coordination mechanisms in connected teams of robots.

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