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 …
Deep multiagent reinforcement learning: Challenges and directions
This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …
combination of deep neural networks with RL has gained increased traction in recent years …
Multi-agent reinforcement learning is a sequence modeling problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …
performance and generalization capabilities in natural language process, vision and …
Rode: Learning roles to decompose multi-agent tasks
Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …
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 …
Celebrating diversity in shared multi-agent reinforcement learning
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …
complex cooperative tasks. Its success is partly because of parameter sharing among …
Consensus of multi-agent systems via fully distributed event-triggered control
This article studies consensus of linear multi-agent systems (MASs) on undirected graphs.
An adaptive event-triggering protocol is constructed for consensus control by using relative …
An adaptive event-triggering protocol is constructed for consensus control by using relative …
Adaptive event-triggered consensus of multiagent systems on directed graphs
This article systematically studies consensus of linear multiagent systems (MASs) on
directed graphs through adaptive event-triggered control. It presents innovative adaptive …
directed graphs through adaptive event-triggered control. It presents innovative adaptive …
Roma: Multi-agent reinforcement learning with emergent roles
The role concept provides a useful tool to design and understand complex multi-agent
systems, which allows agents with a similar role to share similar behaviors. However …
systems, which allows agents with a similar role to share similar behaviors. However …
Dop: Off-policy multi-agent decomposed policy gradients
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However,
there is a significant performance discrepancy between MAPG methods and state-of-the-art …
there is a significant performance discrepancy between MAPG methods and state-of-the-art …