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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Stochastic Games"”.
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Algorithms for Simple Stochastic Games
A simple stochastic game (SSG) is a game defined on a directed multigraph and played between players MAX and MIN. Both players have control over disjoint subsets of vertices: player MAX controls a subset VMAX and player MIN controls a subset VMIN of vertices. The remaining vertices fall into either …
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Some problems in discounted stochastic games
… three problems in the theory of iscounted stochastic games, all motivated by the effects of time-dependent dis-counting. First, it investigates the Nash equilibrium of finite stochastic games with generalised discounting. By employing the framework of gener-alised continuous games, it is …
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Several Reinforcement Learning Methods in Mean-Field Games with Binary Action Spaces
… will be used to study binary action space stochastic games with a sufficiently large number of players that can be generalized to the multi-population case. Based on the mean-field approximation, several algorithms will be implemented and compared in numerical experiments to visualize their …
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Unifying Strategic Military Force Design and Operational Warfighting: A Stochastic Game Approach
… a framework to unify these levels through stochastic games and a force design model. We start with the operational level, where we exploit the structure of military games to construct a tractable representation of the large-scale problem. We develop the campaign stochastic game (CSG), a …
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Robust Control of Stochastic Nonlinear Systems
… minimax optimization problem for a class of stochastic nonlinear systems in strict-feedback form, where in addition to the standard Wiener process there is a norm-bounded unknown disturbance driving the system. The bound on the disturbance is a stochastic integral quadratic constraint, and it …
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Approaches to multi-agent learning
… studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can …
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Stochastic singular control: existence, characterization and approximation of solutions in cost minimization problems and games
Stochastic singular control models (such as optimization problems, games and mean field games) refer to a class of problems in which some agents want to optimize a certain performance criterion by acting in a random environment which evolves in continuous time, and in which the effect of the …
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Game theory with learning for cyber security monitoring
… variations of Q-Learning with a traditional stochastic game model by performing a simulation under different pair of profiles for attackers and defenders using parameters derived from real incident data of a large computer organization. Analysis on the strengths and weaknesses of the …
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On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games
… can be applied to two different types of games. The first type of games are matrix and stochastic games, where the states and actions are represented in discrete domains. In this type of games, we propose two multi-agent reinforcement learning algorithms to solve the problem of learning …
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Risk-averse multi-armed bandits and game theory
… bandits, called explore-then-commit bandits, and stochastic games are studied in this dissertation, which are based on the notion of Risk-Averse Best Action Decision with Incomplete Information (R-ABADI, Abadi is the maiden name of the author's mother). The goal of the classical multi-armed …
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Dynamics and Phenomena in Stateful Multi-Agent Systems
… The underlying environment, strategic or stochastic, may itself also change in response to the sequence of past outcomes, as in ridesharing or information diffusion on networks. The complexities of statefulness induce substantial technical challenges that preclude existing analyses from …
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Statistical Analysis of Wireless Communication Systems Using Hidden Markov Models
… as it has been performed in a new environment. Stochastic game theory is analyzed to gain insight into the decision-making process of HMMs. Study of game theory is significant because it analyzes rational decisions in detail by attaching risk and reward to every possibility. Network security …
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Distributed learning in games under bounded rationality
… We study both cooperative and non-cooperative games under these dual perspectives of bounded rationality and distributed decision-making. In the cooperative setting, we show that globally stable and efficient outcomes can emerge from fully distributed dynamics. Focusing on (i) Transferable …
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Game theoretic analysis and design for network security
… We also study two-player classical and stochastic fictitious play processes which can be viewed as sequences of nonzero-sum matrix games between an Attacker and a Defender. Players do not have access to each other's payoff matrix. Each has to observe the other's actions up to the present …
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Dynamic sequential decision problems with asymmetric information: some existence results
… dynamic sequential decision problems -- games and teams -- with asymmetric information. It is divided into two parts and addresses six broad theoretical problems. In the first part of the thesis, the focus is on dynamic games, in which the decision makers do not observe the same set of …