Search Results for author: Yi Ouyang

Found 13 papers, 2 papers with code

A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems

no code implementations19 Aug 2021 Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang

The regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system.

Scalable regret for learning to control network-coupled subsystems with unknown dynamics

no code implementations18 Aug 2021 Sagar Sudhakara, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang

We consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems connected over a network.

Training a Resilient Q-Network against Observational Interference

1 code implementation18 Feb 2021 Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, Pin-Yu Chen

Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications.

Causal Inference reinforcement-learning

RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS

no code implementations1 Jan 2021 Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama

In this study, we consider a situation in which the agent has access to the generative model which provides us with a next state sample for any given state-action pair, and propose a model to solve a CMDP problem by decomposing the CMDP into a pair of MDPs; \textit{reconnaissance} MDP (R-MDP) and \textit{planning} MDP (P-MDP).

reinforcement-learning

Thompson sampling for linear quadratic mean-field teams

no code implementations9 Nov 2020 Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang

We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i. e., empirical mean) of the states and controls.

Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

no code implementations20 Feb 2020 Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)

Autonomous Navigation online learning +1

Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems

no code implementations27 Jan 2020 Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar

This allows the agents to achieve a regret within $O(\sqrt{T})$ of the regret of the auxiliary single-agent problem.

Multi-agent Reinforcement Learning

Learning Latent State Spaces for Planning through Reward Prediction

no code implementations9 Dec 2019 Aaron Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita

The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning.

Model-based Reinforcement Learning reinforcement-learning

Large-Scale Traffic Signal Offset Optimization

1 code implementation19 Nov 2019 Yi Ouyang, Richard Y. Zhang, Javad Lavaei, Pravin Varaiya

The offset optimization problem seeks to coordinate and synchronize the timing of traffic signals throughout a network in order to enhance traffic flow and reduce stops and delays.

Optimization and Control Systems and Control Systems and Control

Online Learning in Planar Pushing with Combined Prediction Model

no code implementations17 Oct 2019 Huidong Gao, Yi Ouyang, Masayoshi Tomizuka

In this paper, we propose a combined prediction model and an online learning framework for planar push prediction.

online learning Trajectory Planning

Cannot find the paper you are looking for? You can Submit a new open access paper.