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Large sequence models for sequential decision-making: a survey
Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …
purpose sequence models for prediction tasks in natural language processing and computer …
Ace: Cooperative multi-agent q-learning with bidirectional action-dependency
Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which
is the ever-changing targets at every iteration when multiple agents update their policies at …
is the ever-changing targets at every iteration when multiple agents update their policies at …
Flexible ran slicing in open ran with constrained multi-agent reinforcement learning
Network slicing enables the provision of customized services in next-generation mobile
networks. Accordingly, the network is divided into logically isolated networks that share …
networks. Accordingly, the network is divided into logically isolated networks that share …
Is centralized training with decentralized execution framework centralized enough for MARL?
Centralized Training with Decentralized Execution (CTDE) has recently emerged as a
popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where …
popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where …
Tizero: Mastering multi-agent football with curriculum learning and self-play
Multi-agent football poses an unsolved challenge in AI research. Existing work has focused
on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In …
on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In …
A survey on large-population systems and scalable multi-agent reinforcement learning
The analysis and control of large-population systems is of great interest to diverse areas of
research and engineering, ranging from epidemiology over robotic swarms to economics …
research and engineering, ranging from epidemiology over robotic swarms to economics …
More centralized training, still decentralized execution: Multi-agent conditional policy factorization
In cooperative multi-agent reinforcement learning (MARL), combining value decomposition
with actor-critic enables agents to learn stochastic policies, which are more suitable for the …
with actor-critic enables agents to learn stochastic policies, which are more suitable for the …
Controlling behavioral diversity in multi-agent reinforcement learning
The study of behavioral diversity in Multi-Agent Reinforcement Learning (MARL) is a
nascent yet promising field. In this context, the present work deals with the question of how …
nascent yet promising field. In this context, the present work deals with the question of how …
[PDF][PDF] Exploration via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks.
Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning
problems. Previous works argue that complex dynamics between agents and the huge …
problems. Previous works argue that complex dynamics between agents and the huge …
Attention-guided contrastive role representations for multi-agent reinforcement learning
Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …