arXiv:2606.27032v1 Announce Type: new Abstract: 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 compa
This research is emerging now as deep reinforcement learning matures and is increasingly applied to complex, dynamic real-world problems like energy trading, where optimal decision-making is critical.
Sophisticated state representations are crucial for AI agents to effectively navigate volatile markets and operational constraints, directly impacting the profitability and reliability of automated trading systems.
The focus shifts from merely applying DRL to understanding and optimizing the input features for DRL agents in critical infrastructure management.
- · Energy trading firms
- · AI developers specializing in energy
- · Grid operators
- · Inefficient energy arbitrageurs
- · AI models with suboptimal state representations
Improved efficiency and profitability in automated energy trading.
Increased adoption of AI-driven optimization in energy grids, potentially enhancing grid stability and reducing costs.
Accelerated development of generalizable state representation learning techniques for other complex control problems.
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