Survey on machine learning for traffic-driven service provisioning in optical networks
T Panayiotou, M Michalopoulou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The unprecedented growth of the global Internet traffic, coupled with the large spatio-
temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are …
temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are …
Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
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. It first establishes …
learning and decision-making in stochastic games with a large population. It first establishes …
Fictitious play for mean field games: Continuous time analysis and applications
S Perrin, J Pérolat, M Laurière… - Advances in neural …, 2020 - proceedings.neurips.cc
In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to
the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma …
the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma …
Animal-in-the-loop: using interactive robotic conspecifics to study social behavior in animal groups
T Landgraf, GHW Gebhardt, D Bierbach… - Annual Review of …, 2021 - annualreviews.org
Biomimetic robots that replace living social interaction partners can help elucidate the
underlying interaction rules in animal groups. Our review focuses on the use of interactive …
underlying interaction rules in animal groups. Our review focuses on the use of interactive …
Approximately solving mean field games via entropy-regularized deep reinforcement learning
The recent mean field game (MFG) formalism facilitates otherwise intractable computation of
approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time …
approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time …
Learning mean field games: A survey
Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …
applications but remain generally intractable when the number of players increases …
[PDF][PDF] Reinforcement learning in stationary mean-field games
J Subramanian, A Mahajan - … of the 18th International Conference on …, 2019 - cim.mcgill.ca
Multi-agent reinforcement learning (MARL) refers to systems in which multiple agents are
acting in a common and unknown environment. The presence of other agents makes MARL …
acting in a common and unknown environment. The presence of other agents makes MARL …
Concave utility reinforcement learning: The mean-field game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities
in the occupancy measure induced by the agent's policy. This encompasses not only RL but …
in the occupancy measure induced by the agent's policy. This encompasses not only RL but …
Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning
We study infinite horizon discounted mean field control (MFC) problems with common noise
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …