no code implementations • 20 Apr 2024 • Utsav Singh, Wesley A. Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit Singh Bedi
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers.
no code implementations • 18 Mar 2024 • Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Amrit Singh Bedi, Dinesh Manocha
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution-poses a significant challenge for the global convergence of policy gradient methods.
1 code implementation • 6 Mar 2024 • Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju, S. Sivaranjani, Ji Liu, Vijay Gupta, Brian M. Sadler
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory.
1 code implementation • 9 Feb 2024 • Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler
Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer.
no code implementations • 20 Oct 2023 • Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
The hypercomplex PR (HPR) arises in many optical imaging and computational sensing applications that usually comprise quaternion and octonion-valued signals.
no code implementations • 4 Oct 2023 • JohnA. Hodge, Kumar Vijay Mishra, Brian M. Sadler, Amir I. Zaghloul
Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks.
no code implementations • 9 Jun 2023 • Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou, Amrit Singh Bedi, Dinesh Manocha
Trajectory length stands as a crucial hyperparameter within reinforcement learning (RL) algorithms, significantly contributing to the sample inefficiency in robotics applications.
no code implementations • 23 Mar 2023 • Roman Jacome, Edwin Vargas, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver.
no code implementations • 28 Jan 2023 • Wesley A. Suttle, Amrit Singh Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha
Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mixes exponentially fast with a rate parameter that appears in the step-size selection.
no code implementations • ICCV 2023 • Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M. Sadler, Wei-Lun Chao, Yu Su
In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents.
no code implementations • 16 Nov 2022 • Jonathan Monsalve, Edwin Vargas, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
In this dual-blind deconvolution (DBD) problem, the receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets.
no code implementations • 13 Nov 2022 • Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
This paper provides a novel multi-objective inverse reinforcement learning approach which allows for both detection of such Pareto optimal ('coordinating') behavior and subsequent reconstruction of each radar's utility function, given a finite dataset of radar network emissions.
no code implementations • 24 Sep 2022 • Yicheng Chen, Rick S. Blum, Brian M. Sadler
The significant practical advantages of the HB method for learning problems are well known, but the question of reducing communications has not been addressed.
no code implementations • 18 Aug 2022 • Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law).
no code implementations • 22 Jun 2022 • Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program.
no code implementations • 2 Jun 2022 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time.
no code implementations • 2 Jun 2022 • Shenghui Chen, Yagiz Savas, Mustafa O. Karabag, Brian M. Sadler, Ufuk Topcu
We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations.
1 code implementation • CVPR 2022 • Chan Hee Song, Jihyung Kil, Tai-Yu Pan, Brian M. Sadler, Wei-Lun Chao, Yu Su
We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.
no code implementations • 5 Feb 2022 • Yicheng Chen, Rick S. Blum, Brian M. Sadler
Compared to the classical ADMM, a key feature of OADMM is that transmissions are ordered among workers at each iteration such that a worker with the most informative data broadcasts its local variable to neighbors first, and neighbors who have not transmitted yet can update their local variables based on that received transmission.
no code implementations • 5 Feb 2022 • Yicheng Chen, Rick S. Blum, Martin Takac, Brian M. Sadler
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
no code implementations • 27 Jan 2022 • Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform.
no code implementations • 11 Nov 2021 • Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler
Retrieving a signal from its triple correlation spectrum, also called bispectrum, arises in a wide range of signal processing problems.
no code implementations • 11 Nov 2021 • Edwin Vargas, Kumar Vijay Mishra, Roman Jacome, Brian M. Sadler, Henry Arguello
When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters.
no code implementations • 25 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.
1 code implementation • 24 Jun 2021 • Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler
Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively.
no code implementations • 5 Jun 2021 • Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang, Rick S. Blum, Alejandro Ribeiro, Brian M. Sadler
Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data.
no code implementations • 28 Feb 2021 • Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod, Brian M. Sadler, Ufuk Topcu
In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.
no code implementations • 10 Aug 2020 • Yicheng Chen, Rick S. Blum, Brian M. Sadler
The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic.
no code implementations • 27 Mar 2020 • Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler
In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold.
no code implementations • 23 Mar 2020 • Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler, Alec Koppel
Doing so permits us to precisely characterize the trade-off between regret bounds of GP bandit algorithms and complexity of the posterior distributions depending on the compression parameter $\epsilon$ for both discrete and continuous action sets.
no code implementations • 6 Feb 2020 • Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler
More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots.
no code implementations • 17 Oct 2019 • Jiaming Shen, Zeqiu Wu, Dongming Lei, Chao Zhang, Xiang Ren, Michelle T. Vanni, Brian M. Sadler, Jiawei Han
Taxonomies are of great value to many knowledge-rich applications.
no code implementations • pproximateinference AABI Symposium 2019 • Alec Koppel*, Amrit Singh Bedi*, Brian M. Sadler, and Victor Elvira
IS is asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity.
no code implementations • 25 Sep 2019 • Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler
Batch training of machine learning models based on neural networks is now well established, whereas to date streaming methods are largely based on linear models.
no code implementations • 12 Sep 2019 • Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Brian M. Sadler
Prior works control dynamic regret growth only for linear models.
no code implementations • 17 Aug 2018 • Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler
This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph.
no code implementations • 17 Jun 2016 • Alec Koppel, Brian M. Sadler, Alejandro Ribeiro
To do so, we depart from the canonical decentralized optimization framework where agreement constraints are enforced, and instead formulate a problem where each agent minimizes a global objective while enforcing network proximity constraints.
Multiagent Systems Systems and Control Computation