TalkRL: The Reinforcement Learning Podcast NeurIPS 2024 - Posters and Hallways 3
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Mar 9, 2025 Discover innovative benchmarks for multi-agent reinforcement learning in wind farm control, tackling turbine performance issues. Learn about groundbreaking methods that bridge simulation and real-world applications, enhancing exploration strategies. Delve into contextual bi-level reinforcement learning, using leader-follower dynamics for optimizing rewards. Also, explore the QGEN framework, which revolutionizes queuing network simulations with deep learning, setting new standards in action space definition.
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Wake Effects in Wind Farms
- Wake effects in wind farms cause upstream turbines to reduce wind quality for downstream turbines.
- Modeling this as a decentralized multi-agent system maximizes shared energy production.
Static vs Dynamic Wind Models
- Static wind field models assume constant conditions, simplifying observation reconstruction.
- Dynamic models include time evolution, which reveals partial observability complexity.
Learning to Explore via Simulation
- Using simulators to learn exploratory policies enables better real-world reinforcement learning.
- This method significantly weakens conditions needed for successful transfer compared to direct sim-to-real transfer.