Natural Language-Based Human–Machine Collaborative Learning Games Algorithm Based on Deep Rein-Forcement Learning

Human-machine collaborative game agents are usually in an open environment, and they typically obtain behavioral information through environmental rewards. However, traditional agent environment exploration techniques are limited in reward-sparse environments. Deep rein-forcement learning was adopted to design an algorithm with adversarial sparse reward environment rewards and improve the exploration ability and the decision-making ability of agents in electronic game environments. First, a human-machine collaboration model was designed using natural language instructions to guide the rein-forcement learning process of agents based on the concept of reward construction. Then, a hind-sight experience re-play algorithm was introduced to optimize it, solving the reward problem of human-machine collaborative agents in a sparse reward environment. These experiments confirmed that the designed natural language reward construction model could achieve a score of 9.8 in the game and achieve 92% prediction accuracy. The model optimized through hind-sight experience re-play could achieve a maximum accuracy of 97.8% in achieving target instructions and ultimately obtain a game score of 9.9. As a result, the designed natural language human-machine collaboration model has good application performance in coefficient reward environment games and can obtain better scores.