Multi-agent Reinforcement Learning
389 papers with code • 3 benchmarks • 9 datasets
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.
Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Benchmarks
These leaderboards are used to track progress in Multi-agent Reinforcement Learning
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Libraries
Use these libraries to find Multi-agent Reinforcement Learning models and implementationsDatasets
CityFlow
StarCraft II Learning Environment
SMAC-Exp
Hanabi Learning Environment
OG-MARL
pursuitMW
ColosseumRL
Most implemented papers
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
MindSpore-paper-code-3/code2
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In addition, with such modularization, the training algorithm of DeCOM separates the original constrained optimization into an unconstrained optimization on reward and a constraints satisfaction problem on costs.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
iassael/learning-to-communicate
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NeurIPS 2016
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
geek-ai/MAgent
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Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
Mean Field Multi-Agent Reinforcement Learning
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents.
Inequity aversion improves cooperation in intertemporal social dilemmas
eugenevinitsky/sequential_social_dilemma_games
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NeurIPS 2018
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
shariqiqbal2810/MAAC
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ICLR 2019
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings.
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
eugenevinitsky/sequential_social_dilemma_games
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ICLR 2019
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
Reinforcement Learning from Hierarchical Critics
czh513/Hierarchical-Critics-Assignment-for-Multi-agent-Reinforcement-Learning
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Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learning from hierarchical critics (RLHC) algorithm.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently.
FACMAC: Factored Multi-Agent Centralised Policy Gradients
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
