12 citations · 25 across the 6 of their papers we have counts for
7 papers
Multi-agent Actor-Critic with Time Dynamical Opponent Model
Yuan Tian, Klaus-Rudolf Kladny, Qin Wang +2
In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during…
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
Yuan Tian, Minghao Han, Chetan Kulkarni +1
Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing th…
Battery Model Calibration with Deep Reinforcement Learning
Ajaykumar Unagar, Yuan Tian, Manuel Arias-Chao +1
Lithium-Ion (Li-I) batteries have recently become pervasive and are used in many physical assets. To enable a good prediction of the end of discharge of batteries, detailed electro…
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data e…
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
Yuan Tian, Qin Wang, Zhiwu Huang +5
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) ar…
Real-Time Model Calibration with Deep Reinforcement Learning
Yuan Tian, Manuel Arias Chao, Chetan Kulkarni +2
The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational…
Model-free Reinforcement Learning with Robust Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in…