This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Dynamic, Fine-Grained Data Plane Monitoring With Monocle - 2018ABSTRACT:Ensuring network reliability is important for satisfying service-level objectives. However, diagnosing network anomalies during a timely fashion
PROJECT TITLE :Low-power Implementation of Mitchell's Approximate Logarithmic Multiplication for Convolutional Neural Networks - 2018ABSTRACT:This paper proposes an occasional-power implementation of the approximate logarithmic
PROJECT TITLE :Low-Power Approximate Multipliers Using Encoded Partial Products and Approximate Compressors - 2018ABSTRACT:Approximate computing has been thought of to boost the accuracy-performance tradeoff in error-tolerant
PROJECT TITLE :Systematic Design of an Approximate Adder: The Optimized Lower Part Constant-OR Adder - 2018ABSTRACT:Exploiting the tradeoff between accuracy and hardware cost incorporates a tremendous potential to boost the efficiency
PROJECT TITLE :Power Efficient Approximate Booth Multiplier - 2018ABSTRACT:Power consumption is a vital constraint in multimedia and deep learning applications. Approximate computing offers efficient approach to reduce power consumption.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry