Dynamic Information Entropy Optimization: A Reinforcement Learning Approach

Abstract

This paper introduces a novel reinforcement learning algorithm, termed "Dynamic Information Entropy Optimization" (DIEO), that leverages dynamic information entropy to guide the learning process. Traditional reinforcement learning often relies on static reward functions, limiting the agent's ability to adapt to complex and dynamic environments. DIEO dynamically adjusts the reward function based on the current state's information entropy, fostering more effective learning and adaptation. The core mechanism involves quantifying and optimizing the information entropy of the environment, allowing the agent to prioritize key features and responses. This approach offers a significant improvement in learning efficiency and robustness compared to conventional methods, particularly in scenarios with high state uncertainty. We present preliminary results demonstrating the effectiveness of DIEO in a simulated reinforcement learning task involving dynamic environments.