Combining absolute, relative, and forecast price features in the state for Double DQN agents improves arbitrage performance and cross-zone transfer in pumped-storage hydro trading compared to single feature families.
Enhancing battery storage en- ergy arbitrage with deep reinforcement learning and time-series forecasting.arXiv preprint arXiv:2410.20005, 2024
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State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Combining absolute, relative, and forecast price features in the state for Double DQN agents improves arbitrage performance and cross-zone transfer in pumped-storage hydro trading compared to single feature families.