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Found 10 papers, 0 papers with code
Best Ergodic Averages via Optimal Graph Filters in Reversible Markov Chains
In this paper, we address the problem of finding the best ergodic or Birkhoff averages in the ergodic theorem to ensure rapid convergence to a desired value, using graph filters.
Maximum Causal Entropy Inverse Reinforcement Learning for Mean-Field Games
Subsequently, we formulate the maximum casual entropy IRL problem for MFGs - a non-convex optimization problem with respect to policies.
Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion
In this paper, we investigate the robustness of stationary mean-field equilibria in the presence of model uncertainties, specifically focusing on infinite-horizon discounted cost functions.
Common Information Approach for Static Team Problems with Polish Spaces and Existence of Optimal Policies
In this paper, we demonstrate the existence of team-optimal strategies for static teams under observation-sharing information structures.
Linear Mean-Field Games with Discounted Cost
In this paper, we introduce discrete-time linear mean-field games subject to an infinite-horizon discounted-cost optimality criterion.
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a partially observed Markov decision process (POMDP), (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP.
Large Deviations Principle for Discrete-time Mean-field Games
In this paper, we establish a large deviations principle (LDP) for interacting particle systems that arise from state and action dynamics of discrete-time mean-field games under the equilibrium policy of the infinite-population limit.
Q-Learning in Regularized Mean-field Games
In this paper, we introduce a regularized mean-field game and study learning of this game under an infinite-horizon discounted reward function.
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games
In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion.
Learning in Discounted-cost and Average-cost Mean-field Games
We consider learning approximate Nash equilibria for discrete-time mean-field games with nonlinear stochastic state dynamics subject to both average and discounted costs.
