Search Results for author:
Found 18 papers, 6 papers with code
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
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.
Towards Model-Free LQR Control over Rate-Limited Channels
Toward answering this question, we study a setting where a worker agent transmits quantized policy gradients (of the LQR cost) to a server over a noiseless channel with a finite bit-rate.
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.
Learning Decentralized Linear Quadratic Regulator with $\sqrt{T}$ Regret
Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as $\sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern.
Cooperative Actor-Critic via TD Error Aggregation
In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance in neural networks.
Finite-Time Error Bounds for Distributed Linear Stochastic Approximation
While the convergence of consensus-based stochastic approximation algorithms when the interconnection among the agents is described by doubly stochastic matrices (at least in expectation) has been studied, less is known about the case when the interconnection matrix is simply stochastic.
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation
We show that in the presence of Byzantine agents, whose estimation and communication strategies are completely arbitrary, the estimates of the cooperative agents converge to a bounded consensus value with probability one, provided that there are at most $H$ Byzantine agents in the neighborhood of each cooperative agent and the network is $(2H+1)$-robust.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
On the Sample Complexity of Decentralized Linear Quadratic Regulator with Partially Nested Information Structure
We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown.
Spatial modeling of mid-infrared spectral data with thermal compensation using integrated nested Laplace approximation
The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging.
Data-Driven Contract Design for Multi-Agent Systems with Collusion Detection
For a duopoly in which agents are coupled in their payments, we show that if the principal and the agents interact finitely many times, the agents can derive rent by colluding even if the principal knows the types of the agents.
EventGraD: Event-Triggered Communication in Parallel Machine Learning
To relieve some of this overhead, in this paper, we present EventGraD - an algorithm with event-triggered communication for stochastic gradient descent in parallel machine learning.
Adversarial attacks in consensus-based multi-agent reinforcement learning
Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Data-Driven Incident Detection in Power Distribution Systems
In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices.
On Stability and Convergence of Distributed Filters
The paradigm of stability and convergence in distributed filtering is revised in this manuscript.
On the Complexity of Sequential Incentive Design
We model the agent's behavior as a Markov decision process, express its intrinsic motivation as a reward function, which belongs to a finite set of possible reward functions, and consider the incentives as additional rewards offered to the agent.
Optimization and Control
Data-driven Identification of Approximate Passive Linear Models for Nonlinear Systems
In model-based approaches to learning for controller design, it is important to first identify a system model from input-output data.
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.
