An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Smarts: An open-source scalable multi-agent rl training school for autonomous driving
Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …
When physics meets machine learning: A survey of physics-informed machine learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …
of physics, which is the high level abstraction of natural phenomenons and human …
Bi-level actor-critic for multi-agent coordination
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent
reinforcement learning (MARL) methods treat agents equally and the goal is to solve the …
reinforcement learning (MARL) methods treat agents equally and the goal is to solve the …
Modelling bounded rationality in multi-agent interactions by generalized recursive reasoning
Though limited in real-world decision making, most multi-agent reinforcement learning
(MARL) models assume perfectly rational agents--a property hardly met due to individual's …
(MARL) models assume perfectly rational agents--a property hardly met due to individual's …
Model-based multi-agent policy optimization with adaptive opponent-wise rollouts
This paper investigates the model-based methods in multi-agent reinforcement learning
(MARL). We specify the dynamics sample complexity and the opponent sample complexity …
(MARL). We specify the dynamics sample complexity and the opponent sample complexity …
Inducing stackelberg equilibrium through spatio-temporal sequential decision-making in multi-agent reinforcement learning
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish
equilibrium and achieve coordination depending on game structure. However, existing …
equilibrium and achieve coordination depending on game structure. However, existing …
Entropy regularized actor-critic based multi-agent deep reinforcement learning for stochastic games
Multi-agent reinforcement learning (MARL) is an abstract framework modeling a dynamic
environment that involves multiple learning and decision-making agents, each of which tries …
environment that involves multiple learning and decision-making agents, each of which tries …
Learning to play sequential games versus unknown opponents
We consider a repeated sequential game between a learner, who plays first, and an
opponent who responds to the chosen action. We seek to design strategies for the learner to …
opponent who responds to the chosen action. We seek to design strategies for the learner to …
Multi-view reinforcement learning
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for
decision making when agents share common dynamics but adhere to different observation …
decision making when agents share common dynamics but adhere to different observation …