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 Vacuum Filter Set 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 Dryer Humidity sensor 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.