Individual-level inverse reinforcement learning for mean field games
The recent mean field game (MFG) formalism has enabled the application of inverse
reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of …
reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of …
Adversarial inverse reinforcement learning for mean field games
Mean field games (MFGs) provide a mathematically tractable framework for modelling large-
scale multi-agent systems by leveraging mean field theory to simplify interactions among …
scale multi-agent systems by leveraging mean field theory to simplify interactions among …
[PDF][PDF] Maximum entropy inverse reinforcement learning for mean field games
Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in
large-scale multi-agent systems (MAS), through reducing interplays among agents to those …
large-scale multi-agent systems (MAS), through reducing interplays among agents to those …
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Designing suitable reward functions for numerous interacting intelligent agents is
challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field …
challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field …
Partially observable mean field reinforcement learning
Traditional multi-agent reinforcement learning algorithms are not scalable to environments
with more than a few agents, since these algorithms are exponential in the number of …
with more than a few agents, since these algorithms are exponential in the number of …
Learning deep mean field games for modeling large population behavior
We consider the problem of representing collective behavior of large populations and
predicting the evolution of a population distribution over a discrete state space. A discrete …
predicting the evolution of a population distribution over a discrete state space. A discrete …
Scalable deep reinforcement learning algorithms for mean field games
M Lauriere, S Perrin, S Girgin, P Muller… - International …, 2022 - proceedings.mlr.press
Abstract Mean Field Games (MFGs) have been introduced to efficiently approximate games
with very large populations of strategic agents. Recently, the question of learning equilibria …
with very large populations of strategic agents. Recently, the question of learning equilibria …
Reinforcement learning in non-stationary discrete-time linear-quadratic mean-field games
MA uz Zaman, K Zhang, E Miehling… - 2020 59th IEEE …, 2020 - ieeexplore.ieee.org
In this paper, we study large population multiagent reinforcement learning (RL) in the
context of discretetime linear-quadratic mean-field games (LQ-MFGs). Our setting differs …
context of discretetime linear-quadratic mean-field games (LQ-MFGs). Our setting differs …
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Z Wu, M Laurière, SJC Chua, M Geist… - arXiv preprint arXiv …, 2024 - arxiv.org
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but
learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a …
learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a …
Pessimism meets invariance: Provably efficient offline mean-field multi-agent RL
Abstract Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the
applications involving a large population of homogeneous agents, as it exploits the …
applications involving a large population of homogeneous agents, as it exploits the …