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.
Reinforcement learning for electric power system decision and control: Past considerations and perspectives.IFAC-PapersOnLine, 50(1):6918–6927, 2017
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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.