Multi-Agent Reinforcement Learning

Safe multi-agent reinforcement learning for multi-robot control
A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, …
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A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other …
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MANSA: Learning Fast and Slow in Multi-Agent Systems
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the …
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On the complexity of computing Markov perfect equilibrium in general-sum stochastic games
We introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state stochastic games repeated in the infinite horizon and prove its PPAD-completeness.
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A game-theoretic approach to multi-agent trust region optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement …
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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 …
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Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple …
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A Unified Diversity Measure for Multiagent Reinforcement Learning
Promoting behavioural diversity is of critical importance in multi-agent reinforcement learning, since it helps the agent population …
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MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control
We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent environment simulates the target coverage control …
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Offline Pre-trained Multi-agent Decision Transformer
Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access …
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