Journal ArticleDOI
A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems
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TLDR
This paper adapt MCTS and RHO to two problems – a problem inspired by tactical wildfire management and a classical problem involving the control of queueing networks – and undertake an extensive computational study comparing the two methods on large scale instances of both problems in terms of both the state and the action spaces.About:
This article is published in European Journal of Operational Research.The article was published on 2017-12-01. It has received 29 citations till now. The article focuses on the topics: Stochastic optimization & Monte Carlo tree search.read more
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Journal ArticleDOI
Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning
TL;DR: Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions, but controlling multiple autonomous fixed-wing aircraft to maximize efficiency is a challenge.
Proceedings ArticleDOI
Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots
Ravi N. Haksar,Mac Schwager +1 more
TL;DR: This paper proposes a distributed deep reinforcement learning (RL) based strategy for a team of Unmanned Aerial Vehicles (UAVs) to autonomously fight forest fires, and shows with Monte Carlo simulations that the deep RL policy outperforms a hand-tuned heuristic, and scales well for various forest sizes and different numbers of UAVs as well as variations in model parameters.
Posted Content
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
TL;DR: In this paper, two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem of forest fire coverage are presented, where aircraft collaborate on a map of the wildfire's state and maintain a time history of locations visited.
Journal ArticleDOI
Engineering management for high-end equipment intelligent manufacturing
TL;DR: This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment, which include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories, quality and reliability management, information management, and intelligent decision making.
Journal ArticleDOI
Artificial intelligence-based inventory management: a Monte Carlo tree search approach
Deniz Preil,Michael Krapp +1 more
TL;DR: This work develops an offline model as well as an online model which bases decisions on real-time data of Monte Carlo tree search (MCTS) for supply chain inventory management and provides evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.
References
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Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
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Finite-time Analysis of the Multiarmed Bandit Problem
TL;DR: This work shows that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.
Journal ArticleDOI
What is dynamic programming
TL;DR: Sequence alignment methods often use something called a 'dynamic programming' algorithm, which can be a good idea or a bad idea, depending on the method used.
Journal ArticleDOI
A genetic algorithm tutorial
TL;DR: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.
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