Adaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex Scenarios

This study addresses the challenges of dynamics and complexity in intelligent human-computer interaction and proposes a reinforcement learning-based optimization framework to improve long-term returns and overall experience. Human-computer interaction is modeled as a Markov decision process, with state space, action space, reward function, and discount factor defined to capture the dynamics of user input, system feedback, and interaction environment. The method combines policy function, value function, and advantage function, updates parameters through policy gradient, and continuously adjusts during interaction to balance immediate feedback and long-term benefits. To validate the framework, multimodal dialog and scene-aware datasets are used as the experimental platform, with multiple sensitivity experiments conducted on key factors such as discount factor, exploration rate decay, environmental noise, and data imbalance. Evaluation is carried out using cumulative reward, average episode reward, convergence speed, and task success rate. Results show that the proposed method outperforms existing approaches across several metrics, achieving higher task completion while maintaining strategy stability. Comparative experiments further confirm its advantages in interaction efficiency and long-term return, demonstrating the significant value of reinforcement learning in optimizing human-computer interaction.