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Notes on multi-agent reinforcement learning
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One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
The paper discusses a common issue in multi-agent reinforcement learning (MARL) for human-AI interaction: relying on a single large language model to simulate user behavior often leads to poor generalization due to…
Unveiling Complex Collective Behaviors from Simple Rewards
The paper proposes a new framework and analytical tool called EEC and Agent Response Map (ARM) to understand the complex behaviors that can emerge from simple rewards in multi-agent reinforcement learning (MARL). The…
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
The paper introduces a new method called ASALT for multi-agent reinforcement learning (MARL). This method is designed to handle situations where the state-space dimensionalities between source and target domains don't…
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
The paper introduces ASALT, a method for multi-agent reinforcement learning (MARL) that allows for knowledge transfer between source and target domains even if the state space dimensions do not match. ASALT uses…
Decentralized Autonomous Traffic Management through Corridor Networks
Researchers propose a decentralized traffic management system for Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The system allows autonomous aircraft to navigate complex…
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