Multi-agent Reinforcement Learning
336 papers with code • 3 benchmarks • 8 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
ColosseumRL
pursuitMW
Most implemented papers
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
The StarCraft Multi-Agent Challenge
oxwhirl/pymarl
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In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
RLCard: A Toolkit for Reinforcement Learning in Card Games
datamllab/rlcard
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The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
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ICLR 2022
In this paper, we extend the theory of trust region learning to MARL.
Learning with Opponent-Learning Awareness
alshedivat/lola
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We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL.
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
cts198859/deeprl_dist
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ICML 2017
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
cts198859/deeprl_network
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ICML 2018
To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.
