3 research outputs found
Simulation and optimization of robotic tasks for UV treatment of diseases in horticulture
Robotization is increasingly used in the agriculture since the last few decades. It is progressively replacing the human workforce that is deserting the agricultural sector, partly because of the harshness of its activities and health risks they may present. Moreover, robotization aims to improve efficiency and competitiveness of the agricultural sector. However, it leads to several research and development challenges regarding robots supervision, control and optimization. This paper presents a simulation and optimization approach for the optimization of robotized treatment tasks using type-c ultraviolet radiation in horticulture. The optimization of tasks scheduling problem is formalized and a heuristic and a genetic algorithms are proposed to solve it. These algorithms are evaluated compared to an exact method using a multiagent-based simulation approach. The simulator takes into account the evolution of the disease during time and simulates the execution of treatment tasks by the robot.Interreg North-West Europe Programme in context of UV-ROBOT
Hindsight Learning for MDPs with Exogenous Inputs
Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are
exogenous variables outside the control of the decision-maker. We model these
problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and
design a class of data-efficient algorithms for them termed Hindsight Learning
(HL). Our HL algorithms achieve data efficiency by leveraging a key insight:
having samples of the exogenous variables, past decisions can be revisited in
hindsight to infer counterfactual consequences that can accelerate policy
improvements. We compare HL against classic baselines in the multi-secretary
and airline revenue management problems. We also scale our algorithms to a
business-critical cloud resource management problem -- allocating Virtual
Machines (VMs) to physical machines, and simulate their performance with real
datasets from a large public cloud provider. We find that HL algorithms
outperform domain-specific heuristics, as well as state-of-the-art
reinforcement learning methods.Comment: 53 pages, 6 figure