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Showing 1 to 8 of 8 for “"Markov games"”.
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Multi-agent reinforcement learning for nonzero-sum Markov games
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Solving Cyber-Alert Allocation Markov Games with Deep Reinforcement Learning
… Our approach considers a series of sub-games between the attacker and defender in which a state is maintained between each sub-game. We first derive optimal allocation strategies via the use of dynamic programming and Q-maximin value iteration based algorithms. We then move into …
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Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
We study a new class of Markov games, (multi-player) zero-sum Markov Games with Networked separable interactions (zero-sum NMGs), to model the local interaction structure in non-cooperative multi-agent sequential decision-making. We define a zero-sum NMG as a model where the payoffs of the …
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Online Reinforcement Learning in Factored Markov Decision Processes and Unknown Markov Games
… to domains with exact simulators like video games and board games. Even with simulators, the demand for data of RL is sometimes too much a burden, not to mention that for domains in the physical world, like robotics and self-driving, collecting data through interaction is usually costly, in …
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A generalized adversary decision algorithm and analytic solution methods for advise models
… by incorporating theory from discrete-time Markov games. Furthermore, by exploring the state-space and generating the transition probability matrix, numerical solution methods may be applied to solve ADVISE models. Identifying key properties allows the models to be tested for compatibility …
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Bayesian Theory of Mind : modeling human reasoning about beliefs, desires, goals, and social relations
… of approximately rational planning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). …
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Computationally Efficient Reinforcement Learning under Partial Observability
… latent state of the system. Partially observable Markov decision processes (POMDPs) are a generalization of Markov decision processes (MDPs) that model this challenge. Unfortunately, planning and learning near-optimal policies in POMDPs is computationally intractable. Most existing algorithms …
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Near-Optimal Learning in Sequential Games
… decision making problems become sequential games. In this setting, the learning objective shifts from finding an optimal decision rule to finding a Nash equilibrium, where none of the agents can increase their reward by unilaterally switching to another decision rule. To handle both the …