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Full-Text Articles in Engineering
Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar
Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar
Research Collection School Of Computing and Information Systems
Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not …
Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten
Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten
Dissertations and Theses
For the safety of both equipment and human life, it is important to identify the location of orphaned radioactive material as quickly and accurately as possible. There are many factors that make radiation localization a challenging task, such as low gamma radiation signal strength and the need to search in unknown environments without prior information. The inverse-square relationship between the intensity of radiation and the source location, the probabilistic nature of nuclear decay and gamma ray detection, and the pervasive presence of naturally occurring environmental radiation complicates localization tasks. The presence of obstructions in complex environments can further attenuate the …
Near-Optimal Decentralized Power Supply Restoration In Smart Grids, Pritee Agrawal, Akshat Kumar, Pradeep Varakantham
Near-Optimal Decentralized Power Supply Restoration In Smart Grids, Pritee Agrawal, Akshat Kumar, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, enabling decentralized …
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
Shih-Fen CHENG
In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. …
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
Research Collection School Of Computing and Information Systems
In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. …
Distributed Model Shaping For Scaling To Decentralized Pomdps With Hundreds Of Agents, Prasanna Velagapudi, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri
Distributed Model Shaping For Scaling To Decentralized Pomdps With Hundreds Of Agents, Prasanna Velagapudi, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri
Research Collection School Of Computing and Information Systems
The use of distributed POMDPs for cooperative teams has been severely limited by the incredibly large joint policy- space that results from combining the policy-spaces of the individual agents. However, much of the computational cost of exploring the entire joint policy space can be avoided by observing that in many domains important interactions between agents occur in a relatively small set of scenarios, previously defined as coordination locales (CLs) [11]. Moreover, even when numerous interactions might occur, given a set of individual policies there are relatively few actual interactions. Exploiting this observation and building on an existing model shaping algorithm, …