CLEAN Learning to Improve Coordination and Scalability in Multiagent Systems
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Summary
This work introduces Coordinated Learning without Exploratory Action Noise (CLEAN) rewards which improve coordination and performance by utilizing the concept of private exploration in order to remove the negative impact of traditional “public” exploration strategies from learning in multiagent systems.
- Type
- dissertation
- Published
- 2013-04-15
- Cited by
- 3
- References
- 151
- OpenAlex
- https://openalex.org/W640859031
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:106674872
Keywords
Scalability, Multi-agent system, Computer science, Distributed computing, Human–computer interaction
References
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- Maximising Sensor Network Efficiency Through Agent-Based Coordination of Sense/Sleep Schedules
- A Solar-Powered HALE-UAV for Arctic Research
- QUICR-Learning for Multi-Agent Coordination
- Entropy based anomaly detection applied to space shuttle main engines
- Reward shaping for valuing communications during multi-agent coordination
- Scaling multi-agent learning in complex environments
- Pattern Recognition and Machine Learning
- Model-Based Bayesian Reinforcement Learning in Large Structured Domains
- Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
- Integrating Sample-Based Planning and Model-Based Reinforcement Learning
- Non-Stationary Policy Learning in 2-Player Zero Sum Games
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