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Found 7 papers, 2 papers with code
Fully Independent Communication in Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems.
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments
Although still in simulation, the investigated situations are conceptually closer to real scenarios and thus, with these results, we intend to motivate further research in the subject.
Learning Independently from Causality in Multi-Agent Environments
Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning.
Discovering Causality for Efficient Cooperation in Multi-Agent Environments
In this paper, we investigate the applications of causality in MARL and how it can be applied in MARL to penalise these lazy agents.
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football
The experiment results in the GRF environment prove that our reward shaping method is a useful addition to state-of-the-art MARL algorithms for training agents in environments with sparse reward signal.
Causality Detection for Efficient Multi-Agent Reinforcement Learning
When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team.
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning
The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN and VDN, in a range of multi-agent cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
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