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About Smart Grid Optimiization by Deep Reinforcement Learning Over Discreet and Continuous Action Space
The document discusses applying deep reinforcement learning algorithms to optimize smart grids. It develops two algorithms: 1) Deep Q-learning for discrete action spaces like switching power supply/sell and battery charge/discharge. 2) A Gibbs deep policy gradient algorithm for continuous action spaces, which hybridizes deterministic and stochastic policy gradients. Both algorithms are embedded in a rigorous physical model to optimize smart grid energy management. The results showed the agent learned to maximize rewards by capturing energy demand and supply patterns.
Detailed Information
| Author: | ['Simbarashe Nguruve'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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