Name
Abstract | ||
|---|---|---|
•Presents a solution for security games employing partially observable Markov chains.•Implements a reinforcement learning algorithm.•Suggests two adaptive rules one for the transition matrix and one for the estimated costs.•Employs a proximal/gradient algorithm to compute the equilibrium strategies of the game.•Formulates a new patrolling planner in terms of a random walk based on partial information. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ress.2019.106695 | Reliability Engineering & System Safety |
Keywords | Field | DocType |
Reinforcement learning,Incomplete information,Security games. | Convergence (routing),Rationality,Scheduling (computing),Behavioral modeling,Markov chain,Theoretical computer science,Engineering,Perfect information,Stackelberg competition,Reliability engineering,Reinforcement learning | Journal |
Volume | ISSN | Citations |
195 | 0951-8320 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillermo Alcantara-Jiménez | 1 | 1 | 0.35 |
| Julio B. Clempner | 2 | 91 | 20.11 |