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Found 9 papers, 1 papers with code
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games
MF-OML is the first fully polynomial multi-agent reinforcement learning algorithm for provably solving Nash equilibria (up to mean-field approximation gaps that vanish as the number of players $N$ goes to infinity) beyond variants of zero-sum and potential games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
MESOB: Balancing Equilibria & Social Optimality
Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation.
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models
Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e. g., age, region, time, forecast horizon, etc.
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
Our result is the first global convergence and sample complexity results for the stochastic entropy-regularized vanilla PG method.
On the Global Optimum Convergence of Momentum-based Policy Gradient
For the generic Fisher-non-degenerate policy parametrizations, our result is the first single-loop and finite-batch PG algorithm achieving $\tilde{O}(\epsilon^{-3})$ global optimality sample complexity.
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
To our best knowledge, this is the first theoretical guarantee on fictitious discount algorithms for the episodic reinforcement learning of finite-time-horizon MDPs, which also leads to the (first) global convergence of policy gradient methods for finite-time-horizon episodic reinforcement learning.
Sample Efficient Reinforcement Learning with REINFORCE
Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory.
A General Framework for Learning Mean-Field Games
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population.
Robust Super-Level Set Estimation using Gaussian Processes
This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability.
