Game Theory and Multi-agent Reinforcement Learning

By Ann Nowé, Peter Vrancx, Yann-Michaël De Hauwere. 2012

210 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

The Hanabi Challenge: A New Frontier for AI Research (2019, background): “Several years of work on MARL gave rise to many algorithms, extensions, and analyses [59, 60, 61, 62, 63].”
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles (2020, background): “Many real-world systems can be modeled as multi-agent systems (MAS), such as autonomous vehicle teams (Cao et al., 2012), intelligent warehouse systems (Now´e et al., 2012), and sensor networks (Zhang & Lesser, 2011).”
A Survey on Traffic Signal Control Methods (2019, background): “…becomes complicated, the non-stationary impacts from neighboring agents will be brought into the environment and the learning process usually cannot converge to stationary policies if there are no communication or coordination mechanisms among agents [Bishop et al. 2006; Nowe et al. 2012].”
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks (2018, methodology): “In this subsection, we consider to model the formulated problem (15) by adopting a stochastic game (also called Markov game) framework [25], since it is the generalization of the Markov decision processes to the multi-agent case.”
Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage (2018, methodology): “In MARL, the action-state value function of each agent also depends on the joint state and joint action [30], represented as: where 0 γ ≤ 1 is the discount factor of the learning.”
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018, background): “Consequently, game-theoretic formalisms are often used as the basis for representing interactions and decision-making in multiagent systems [17, 79, 64].”
A Deep Policy Inference Q-Network for Multi-Agent Systems (2017, methodology): “gly. This further necessitates a robust methodology to model collaborators or opponents in an MAS. There is a significant body of work on MAS. The literature contains numerous studies of MAS modeling [7, 10, 27] and investigations of non-stationarity issues [16]. Most of previous researches on oppon”
CoLight: Learning Network-level Cooperation for Traffic Signal Control (2019, background): “To avoid the non-stationary impacts from neighboring agents, communication among agents [2, 19] are proposed in order to achieve coordination using neighboring information: [5, 8, 9, 22, 31] add downstream information into states, [1, 31] add all neighboring states, and [18] adds neighbors’ hidden s”
Influencing Long-Term Behavior in Multiagent Reinforcement Learning (2022, unknown): “Indeed, even when agents do act rationally, multiple equilibria can exist for a single game with some of these Pareto dominating others (Now´e et al., 2012).”
Influence-Based Multi-Agent Exploration (2019, background): “…been focusing on single-agent scenarios and leave the exploration problem largely unstudied for multi-agent settings – it is common in real-world applications that multiple agents are required to solve a task in a coordinated fashion (Cao et al., 2012; Now´e et al., 2012; Zhang & Lesser, 2011).”
Algorithms for computing strategies in two-player simultaneous move games (2016, unknown): “For surveys of the relevant previous work in multiagent reinforcement learning and game theory (including the zero-sum case), see [30, 31, 32].”