State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading

2026-06-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Energy & Utilities · Depth: Expert, quick

Summary

A study on deep reinforcement learning (DRL) for energy trading highlights the critical role of state representation in achieving robust performance and transferability. Using a fixed Double DQN agent within the HydroDam pumped-storage arbitrage environment, researchers compared various market feature families: absolute price/calendar, relative price context, and short-horizon forecast information. Policies were trained on 2007-2011 Belgian day-ahead prices and evaluated on a 2012-2025 test set and 39 other ENTSO-E market zones. Results showed that single feature families performed poorly, with absolute features reaching only 28.8% on the test set and a 5.7% cross-zone median. However, combining features significantly improved outcomes: absolute + relative achieved 49.9% on the test set and 39.8% cross-zone median, while absolute + relative + forecast reached 55.6% and 47.5%, respectively. This demonstrates that combining diverse feature families is essential for effective DRL in energy trading.

Key takeaway

For Machine Learning Engineers developing DRL agents for energy trading, you should prioritize a multi-faceted state representation. Relying solely on absolute prices or forecasts will severely limit your agent's performance and transferability. Instead, integrate absolute price scales, recent relative price context, and short-horizon forecast information to achieve robust policies that perform well across diverse market conditions and geographic zones.

Key insights

Effective DRL for energy trading critically depends on combining diverse state representation features for robust performance.

Principles

• State representation is central to DRL policy design.
• Robust transfer requires combining feature families.
• Single feature families underperform in complex markets.

Method

A fixed Double DQN agent was used in a pumped-storage arbitrage environment (HydroDam) to compare absolute, relative, and forecast market features, and their combinations, keeping other parameters constant.

In practice

• Combine price scale, relative context, and forecast data.
• Test DRL agents across multiple market zones.
• Evaluate feature combinations, not just single types.

Topics

• Deep Reinforcement Learning
• Energy Trading
• State Representation
• Double DQN
• HydroDam
• Market Features

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.