Search Results for author: P. R. Kumar

Found 19 papers, 4 papers with code

TERRA: Beam Management for Outdoor mm-Wave Networks

no code implementations10 Jan 2023 Santosh Ganji, Jaewon Kim, Romil Sonigra, P. R. Kumar

To avoid outage in transient pedestrian blockage of the LoS path, the mobile uses reflected or NLoS path available in indoor environments.


Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

no code implementations2 Nov 2022 Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu, Mengdi Wang, Vijay Vittal, P. R. Kumar, Srinivas Shakkottai, Yi Cui

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.

Decision Making

Terra: Blockage Resilience in Outdoor mmWave Networks

1 code implementation25 Sep 2022 Santosh Ganji, Jaewon Kim, P. R. Kumar

This allows the mobile to maintain time-synchronization with the base station, allowing it to revert to the LoS path when the temporary blockage disappears.

Detect Ground Reflections

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning

1 code implementation10 Jun 2022 Ruida Zhou, Tao Liu, Dileep Kalathil, P. R. Kumar, Chao Tian

We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off.

Fairness reinforcement-learning +1

On an Information and Control Architecture for Future Electric Energy Systems

no code implementations1 Jun 2022 Le Xie, Tong Huang, P. R. Kumar, Anupam A. Thatte, Sanjoy K. Mitter

This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification.

Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems

no code implementations25 Jan 2022 Akshay Mete, Rahul Singh, P. R. Kumar

We re-examine an approach called "Reward-Biased Maximum Likelihood Estimate" (RBMLE) that was proposed more than forty years ago, and which predates the "Upper Confidence Bound" (UCB) method as well as the definition of "regret".

Thompson Sampling

Policy Optimization for Constrained MDPs with Provable Fast Global Convergence

no code implementations31 Oct 2021 Tao Liu, Ruida Zhou, Dileep Kalathil, P. R. Kumar, Chao Tian

We propose a new algorithm called policy mirror descent-primal dual (PMD-PD) algorithm that can provably achieve a faster $\mathcal{O}(\log(T)/T)$ convergence rate for both the optimality gap and the constraint violation.

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

1 code implementation27 Sep 2021 Tao Liu, P. R. Kumar, Ruida Zhou, Xi Liu

Motivated by the problem of learning with small sample sizes, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful.

Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs

no code implementations NeurIPS 2021 Tao Liu, Ruida Zhou, Dileep Kalathil, P. R. Kumar, Chao Tian

We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$.

Safe Exploration

An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification

no code implementations22 May 2021 Lu Zhang, Bin Wang, Vivek Sarin, Weiping Shi, P. R. Kumar, Le Xie

In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i. e., a set of linear equations.

Reward Biased Maximum Likelihood Estimation for Reinforcement Learning

no code implementations16 Nov 2020 Akshay Mete, Rahul Singh, Xi Liu, P. R. Kumar

The Reward-Biased Maximum Likelihood Estimate (RBMLE) for adaptive control of Markov chains was proposed to overcome the central obstacle of what is variously called the fundamental "closed-identifiability problem" of adaptive control, the "dual control problem", or, contemporaneously, the "exploration vs. exploitation problem".

Multi-Armed Bandits reinforcement-learning +2

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits

no code implementations8 Oct 2020 Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu, P. R. Kumar

Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems.

Learning in Networked Control Systems

no code implementations21 Mar 2020 Rahul Singh, P. R. Kumar

We design adaptive controller (learning rule) for a networked control system (NCS) in which data packets containing control information are transmitted across a lossy wireless channel.

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits

no code implementations2 Jul 2019 Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya, P. R. Kumar

To choose the bias-growth rate $\alpha(t)$ in RBMLE, we reveal the nontrivial interplay between $\alpha(t)$ and the regret bound that generally applies in both the Exponential Family as well as the sub-Gaussian/Exponential family bandits.

Multi-Armed Bandits

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging

1 code implementation29 Oct 2018 Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar

Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection.

Decision Making Multi-Armed Bandits

Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: II Wireless Networks with Interference

no code implementations6 Sep 2017 Rahul Singh, P. R. Kumar, Eytan Modiano

The key difference arises due to the fact that in our set-up the packets loose their utility once their "age" has crossed their deadline, thus making the task of optimizing timely throughput much more challenging than that of ensuring network stability.


Belief Space Planning Simplified: Trajectory-Optimized LQG (T-LQG) (Extended Report)

no code implementations10 Aug 2016 Mohammadhussein Rafieisakhaei, Suman Chakravorty, P. R. Kumar

Planning under motion and observation uncertainties requires solution of a stochastic control problem in the space of feedback policies.

Robotics Optimization and Control

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