## Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms

Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms finite-sample convergence rates for q-learning and indirect algorithms

July 26, 2019

Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms finite-sample convergence rates for q-learning and indirect algorithms

July 26, 2019

Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Run Time Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Run Time The nonlinear Bellman equation = linear programming problem: Primal-Dual LP Primal LP (1) Dual LP (2) Minmax Problem (3)

March 8, 2019

Decentralized Optimal Control of Distributed Interdependent Automata With Priority Structure Data Flowchart Notation : subsystem model, the plant P i , deterministic finite-state automaton. (1) (2) (3) (4) : P i can be transitioned from state into state if the input l is applied. (5) It encodes with that the transition is possible with at least… read more »

March 15, 2019

Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics – Math and Optimal Control Problem formulation Consider a continuous-time nonlinear large-scale system ∑ composed of N interconnected subsystems described by (1) where xi(t) ∈ Rni : state. The overall state of the large-scale system ∑ is denoted by ui [ xi(t) ] ∈ Rmi : control input vector of the ith… read more »

March 5, 2019

Reinforcement Learning is Direct Adaptive Optimal Control Stanford_cs229-notes12_Andrew_Ng Reinforcement Learning and Control How should Reinforcement learning be viewed from a control systems perspective? Control problems can be divided into two classes: regulation and tracking problems, in which the objective is to follow a reference trajectory. optimal control problems, which the objective is to extremize a… read more »

February 28, 2019

Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach Neural-network-based Online Learning Optimal Control Decentralized Control Strategy Cost functions (critic neural networks) – local optimal controllers Feedback gains to the optimal control policies – decentralized control strategy Optimal Control Problem (Stabilization) Hamilton-Jacobi-Bellman (HJB) Equations Apply Online Policy Iteration… read more »