Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning
Jacopo Castellini1, Frans A Oliehoek2, Rahul Savani1
1Department of Computer Science, University of Liverpool, Liverpool, UK.
Autonomous Agents and Multi-Agent Systems
|November 1, 2021
Summary
Deep reinforcement learning in multi-agent systems shows promise, but understanding network learning is key. This study investigates network architectures in one-shot games to improve cooperative AI performance.
Area of Science:
- Artificial Intelligence
- Multi-Agent Systems
- Deep Reinforcement Learning
Background:
- Deep reinforcement learning (DRL) has been successfully applied to cooperative multi-agent systems.
- Theoretical understanding of DRL in these systems is limited, hindering performance enhancement.
Purpose of the Study:
- To empirically investigate the learning capabilities of different neural network architectures.
- To identify factors limiting DRL performance in cooperative multi-agent settings.
Main Methods:
- Analysis of various neural network architectures.
- Empirical evaluation on a series of one-shot games.
- Quantification of value function representation by different approaches.
Main Results:
- Identified limitations in network architectures for representing value functions in multi-agent scenarios.
- Highlighted issues like value sparsity and overly strict coordination requirements impede performance.
- Extended previous findings on DRL in cooperative multi-agent systems.
Conclusions:
- Understanding network learning is crucial for advancing DRL in multi-agent systems.
- Specific network architectures and game characteristics significantly impact cooperative AI performance.
- Results provide insights for designing more effective DRL agents for complex coordination tasks.
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