Neuro-Dynamic Programming for Radiation Treatment Planning

In many cases a radiotherapy treatment is delivered as a series of smaller dosages over a period of time. Currently, it is difficult to determine the actual dose that has been delivered at each stage, precluding the use of adaptive treatment plans. However, new generations of machines will give more accurate information of actual dose delivered, allowing a planner to compensate for errors in delivery. We formulate a model of the day-to-day planning problem as a stochastic linear program and exhibit the gains that can be achieved by incorporating uncertainty about errors during treatment into the planning process. Due to size and time restrictions, the model becomes intractable for realistic instances. We show how neuro-dynamic programming can be used to approximate the stochastic solution, and derive results from our models for realistic time periods. These results allow us to generate practical rules of thumb that can be immediately implemented in current planning technologies.\ud \ud This material is based on research partially supported by the National Science Foundation Grants ACI-0113051 and CCR-9972372, the Air Force Office of Scientific Research Grant F49620-01-1-0040, Microsoft Corporation and the Guggenheim Foundation

Optimal Control of a Fed-batch Fermentation Process by Neuro-Dynamic Programming

In this paper the method for optimal control of a fermentation process is presented, that is based on an approach for optimal control - Neuro-Dynamic programming. For this aim the approximation neural network is developed and the decision of the optimization problem is improved by an iteration mode founded on the Bellman equation. With this approach computing time and procedure are decreased and quality of the biomass at the end of the process is increased

Play selection in football : a case study in neuro-dynamic programming

Includes bibliographical references (p. 34-35).Supported by the US Army Research Office. AASERT-DAAH04-93-GD169Stephen D. Patek, Dimitri P. Bertsekas

Neuro-dynamic programming for cooperative inventory control

In Multi-Retailer Inventory Control the possibility of sharing set up costs motivates communication and coordination among the retailers. We solve the problem of finding suboptimal distributed reordering policies which minimize set up, ordering, storage and shortage costs, incurred by the retailers over a finite horizon. Neuro-Dynamic Programming (NDP) reduces the computational complexity of the solution algorithm from exponential to polynomial on the number of retailers

Self-Learning Neural controller for Hybrid Power Management using Neuro-Dynamic Programming

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the cost-to-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89874/1/draft_01.pd