A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
On imitation in mean-field games
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs),
where the goal is to imitate the behavior of a population of agents following a Nash …
where the goal is to imitate the behavior of a population of agents following a Nash …
Maximum Causal Entropy Inverse Reinforcement Learning for Mean-Field Games
In this paper, we introduce the maximum casual entropy Inverse Reinforcement Learning
(IRL) problem for discrete-time mean-field games (MFGs) under an infinite-horizon …
(IRL) problem for discrete-time mean-field games (MFGs) under an infinite-horizon …
Adaptive generative adversarial maximum entropy inverse reinforcement learning
L Song, D Li, X Xu - Information Sciences, 2025 - Elsevier
Maximum entropy inverse reinforcement learning algorithms have been extensively studied
for learning rewards and optimizing policies using expert demonstrations. However, high …
for learning rewards and optimizing policies using expert demonstrations. However, high …
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory
MM Çelikok, FA Oliehoek, JW van de Meent - arXiv preprint arXiv …, 2024 - arxiv.org
We consider inverse reinforcement learning problems with concave utilities. Concave Utility
Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which …
Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which …
Scalable Offline Reinforcement Learning for Mean Field Games
Reinforcement learning algorithms for mean-field games offer a scalable framework for
optimizing policies in large populations of interacting agents. Existing methods often depend …
optimizing policies in large populations of interacting agents. Existing methods often depend …
Imitation Learning for Mean Field Games with Correlated Equilibria
Imitation learning (IL) is a powerful approach for acquiring optimal policies from
demonstrated behaviors. However, applying IL to a large group of agents is arduous due to …
demonstrated behaviors. However, applying IL to a large group of agents is arduous due to …
[引用][C] From One to Infinity: New Algorithms for Reinforcement Learning and Inverse Reinforcement Learning
Y Chen - 2022 - ResearchSpace@ Auckland