Multi-agent Reinforcement Learning
380 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.
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Libraries
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Most implemented papers
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
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.
QPLEX: Duplex Dueling Multi-Agent Q-Learning
This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions.
A multi-agent reinforcement learning model of common-pool resource appropriation
Here we show that deep reinforcement learning can be used instead.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Learning Transferable Cooperative Behavior in Multi-Agent Teams
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box.
MAVEN: Multi-Agent Variational Exploration
We specifically focus on QMIX [40], the current state-of-the-art in this domain.
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.