arXiv research

A new multi-agent learning method improves performance in complex games.

The paper formalizes and analyzes multi-agent Q-learning with value factorization.

PPO algorithm converges to global optimality in multi-agent reinforcement learning.

FACMAC combines deep policy gradients with factored critic for multi-agent reinforcement learning.

This work benchmarks MARL algorithms in cooperative tasks.

This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-…

MARL algorithm uses regularization to avoid explicit structures, improving performance.

Improved exploration in cooperative multi-agent reinforcement learning.

CODA resolves coordination issues in offline multi-agent reinforcement learning.

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-…

Paper tackles delays in multi-agent reinforcement learning, improving performance.

New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.

NeuroMAS treats multi-agent systems as neural networks for scalable, trainable coordination.

Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism…

In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a small number of agents. When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these met…

A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…

The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.

Flexible decentralized MARL framework for cooperative multi-agent learning.

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agent…

AutoDIME automates design of multi-agent environments for RL.

Generative thermal design learns optimal shapes using multi-agent reinforcement learning.

Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their ac…

Study differential privacy in multi-agent RL, achieving efficient and private learning.

PettingZoo library accelerates multi-agent reinforcement learning research.

Decentralized Gaussian processes for multi-agent systems.

VPP learns joint policies for multi-agent RL through interactions.

A novel framework for adaptive multi-agent communication in reinforcement learning.

QTRAN++ improves MARL performance in complex environments.

Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-agent learning could help multi-agent systems become both efficient and stable. However, learning efficiency and fairness simultaneously is a …

Central to all machine learning algorithms is data representation. For multi-agent systems, selecting a representation which adequately captures the interactions among agents is challenging due to the latent group structure which tends to vary depending on context. However, in multi-agent systems with strong group stru…

A scalable MARL algorithm using local rewards for cooperative multi-agent learning.

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic…

EMIX minimizes surprise in multi-agent reinforcement learning.

New NPG variants ensure parameter convergence in multi-agent learning.

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increa…

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal pol…

We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations. We formulate a multi-agent system as a coupled non-linear network with a generic ordinary differential equation (ODE) based state evolut…

New algorithm reduces learning regret in multi-agent systems with unknown dynamics.

New method uses LP to achieve optimal sample complexity in multi-agent reinforcement learning.

Study collaborative learning among multi-agents in multi-armed bandits.

Study compares MAPF and MARL algorithms for warehouse automation.

Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (…

CollaQ improves multi-agent performance in StarCraft by 40% with fewer samples.

Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current methods often fail to efficiently learn policies. We thus investigate the presence of a common weakness in single-agent RL, namely value fu…

New methods improve multi-agent reinforcement learning by addressing rotational dynamics.

AgensFlow learns multi-agent coordination policies from experience.

This paper proposes using variational autoencoders to model opponents in multi-agent systems.

The paper develops an economic foundation for multi-agent learning in markets.