Research on Entity Control Algorithm of Cross-Domain Knowledge Graph Based on Reinforcement Learning

This study introduces a novel algorithm rooted in reinforcement learning to address the entity control challenge within cross-domain knowledge graphs. The proposed approach integrates the Q-Learning algorithm with the Actor-Critic framework, achieving precise optimization of entity control within multi-domain knowledge graphs. The paper provides an in-depth analysis of the state-action value function’s update mechanism in the Q-Learning algorithm, as well as the cooperative operational dynamics of the Actor-Critic framework. By devising an appropriate reward function and state transition model, the agent’s learning trajectory across different domain knowledge graphs is refined, resulting in enhanced algorithmic performance. In the simulation tests, this paper conducts a comparative analysis of the accuracy, robustness, and overall efficiency of the Q-Learning algorithm and the Actor-Critic approach within cross-domain knowledge graphs. The experimental outcomes indicate that the Q-Learning algorithm outperforms in both precision and robustness, demonstrating superior resilience in the presence of noise and uncertainty across various domains. These findings clearly illustrate the practical applicability and distinct advantages of the Q-Learning algorithm in the task of entity control in cross-domain knowledge graphs.