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Markov α-Potential Games
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/1-84628-095-8_9openalex-snapshot
Randomized Algorithms for Semi-Infinite Programming Problems
inferredcites →https://doi.org/10.1007/bf01737559openalex-snapshot
A class of games possessing pure-strategy Nash equilibria
inferredcites →https://doi.org/10.1016/0016-0032(53)90617-6openalex-snapshot
Principles of mathematical analysis
inferredcites →https://doi.org/10.1016/j.ifacol.2022.11.031openalex-snapshot
Gradient Play in Stochastic Games: Stationary Points and Local Geometry
inferredcites →https://doi.org/10.1017/cbo9780511800481.004openalex-snapshot
The Complexity of Finding Nash Equilibria
inferredcites →https://doi.org/10.1109/cdc51059.2022.9992762openalex-snapshot
Multi-Agent Learning via Markov Potential Games in Marketplaces for Distributed Energy Resources
inferredcites →https://doi.org/10.1109/tac.2016.2598476openalex-snapshot
Decentralized Q-Learning for Stochastic Teams and Games
inferredcites →https://doi.org/10.1109/tac.2021.3121228openalex-snapshot
Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games With Local Control and Global State Information
inferredcites →https://doi.org/10.1109/tac.2025.3576379openalex-snapshot
Independent and Decentralized Learning in Markov Potential Games
inferredcites →https://doi.org/10.1137/1035089openalex-snapshot
Semi-Infinite Programming: Theory, Methods, and Applications
inferredcites →https://doi.org/10.1137/22m1515112openalex-snapshot
Satisficing Paths and Independent Multiagent Reinforcement Learning in Stochastic Games
inferredcites →https://doi.org/10.1287/moor.1110.0500openalex-snapshot
Flows and Decompositions of Games: Harmonic and Potential Games
inferredcites →https://doi.org/10.1287/moor.2016.0778openalex-snapshot
Learning in Games via Reinforcement and Regularization
inferredcites →https://doi.org/10.15607/rss.2021.xvii.084openalex-snapshot
Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories
inferredcites →https://doi.org/10.23919/acc60939.2024.10644523openalex-snapshot
Distributed Multi-Agent Interaction Generation with Imagined Potential Games
inferredcites →https://doi.org/10.23919/ecc.2003.7086500openalex-snapshot
Randomized algorithms for semi-infinite programming problems
inferredcites →https://doi.org/10.48550/arxiv.1802.00899openalex-snapshot
Learning Parametric Closed-Loop Policies for Markov Potential Games
inferredcites →https://doi.org/10.48550/arxiv.2101.04233openalex-snapshot
Independent Policy Gradient Methods for Competitive Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.2105.15186openalex-snapshot
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
inferredcites →https://doi.org/10.48550/arxiv.2106.01969openalex-snapshot
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
inferredcites →https://doi.org/10.48550/arxiv.2110.04184openalex-snapshot
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
inferredcites →https://doi.org/10.48550/arxiv.2110.10614openalex-snapshot
Independent Natural Policy Gradient Always Converges in Markov Potential Games
inferredcites →https://doi.org/10.48550/arxiv.2202.04129openalex-snapshot
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
inferredcites →https://doi.org/10.48550/arxiv.2411.03669openalex-snapshot
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
inferredcites →https://doi.org/10.52202/068431-0140openalex-snapshot
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
inferredcites →https://doi.org/10.52202/068431-0800openalex-snapshot
Learning in Congestion Games with Bandit Feedback
inferredcites →https://doi.org/10.52202/075280-1907openalex-snapshot
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
inferredcites →https://doi.org/10.65109/fppp1154openalex-snapshot
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-agent Reinforcement Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4642667openalex-snapshot
Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
inferred← citeshttps://doi.org/10.2139/ssrn.5199838openalex-snapshot
An α-Potential Game Framework for N-player Dynamic Games
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