This paper explores the use of Reinforcement Learning (RL) to optimize data center operations integrated with wind farms. Researchers developed a simulation framework to test RL agents for workload shifting, aiming to maximize the use of wind energy while accounting for curtailment. The study found that while RL agents like Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) showed strong performance, they still lagged behind offline optimizers due to their online decision-making limitations. The paper also evaluated Imitation Learning and Reward Shaping as methods to improve RL performance. AI
IMPACT This research could lead to more efficient energy management in data centers, reducing operational costs and environmental impact by better integrating renewable energy sources.
RANK_REASON The cluster contains a research paper detailing a novel application of reinforcement learning.
- Data Centers
- Imitation Learning
- Proximal Policy Optimization
- Reinforcement Learning
- Soft Actor-Critic
- Wind Farms
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