Abstract
This study addresses the complexities of carbon quota trading markets amidst global warming concerns, proposing a deep reinforcement learning (DRL)-based decision-making model to enhance trading strategies. Acknowledging the limitations of conventional methods in navigating volatile carbon prices, policy shifts, and informational disparities, the research integrates DRL's advanced capabilities. It commences with an overview of DRL principles and its successful applications, followed by an analysis of market dynamics and trading nuances. A DRL model is then formulated, delineating state-action spaces and a tailored reward function for optimized learning within the carbon trading context. Model refinement involves hyperparameter tuning for superior performance. The summary concludes with an evaluation of the model's efficacy, highlighting its adaptability and computational demands, while outlining avenues for further enhancement and real-world implementation to combat climate change through improved carbon market operations.
Published Version
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