State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
For researchers applying deep RL to energy trading, this work demonstrates that state representation is a critical design choice, not a minor preprocessing step, and provides concrete evidence that combining multiple feature families is necessary for robust policy transfer.
The paper shows that combining absolute price features, relative price context, and short-horizon forecasts in the state representation significantly improves the performance and transferability of a Double DQN agent for pumped-storage arbitrage, achieving up to 55.6% on the test set and 47.5% cross-zone median, compared to 28.8% and 5.7% with absolute features alone.
Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This makes the state representation given to a reinforcement learning agent an important design choice. We study this in HydroDam, a pumped-storage arbitrage environment, using a fixed Double DQN agent. The environment, action space, reward function, network, and training protocol are kept fixed; only the market features are changed. We compare absolute price/calendar features, relative features that compare current prices with recent market history, forecast features, and all combinations of these three feature families. Policies are trained and selected using 2007--2011 Belgian day-ahead prices and evaluated on two test settings: a later same-market test set from 2012--2025 and 39 other ENTSO-E market zones. Absolute features only reaches 28.8% on the test set and a median 5.7% across zones. Relative-only and forecast-only states also stay below a rolling price-score heuristic in the cross-zone median. Combining feature families is much stronger: absolute + relative reaches 49.9% on the test set and a 39.8% cross-zone median, while absolute + relative + forecast reaches 55.6% and 47.5%. These results suggest that state representation is not a minor preprocessing choice in storage-trading RL, but a central part of the policy design: robust transfer requires combining price scale, recent relative price context, and short-horizon forecast information, rather than relying on any single feature family.