Aiming at the limitations of traditional imitation learning approaches in handling diverse expert trajectories,particularly the difficulty in effectively integrating fixed-modality expert data of varying quality,this paper innovatively integrates the multiple trajectories generative adversarial imitation learning(MT-GAIL)method with temporal-difference error behavioral cloning(TD-BC)technology to construct a hybrid imitation learning framework.This framework not only enhances the model's adaptability to complex and dynamic expert strategies but also improves its robustness in extracting useful information from low-quality data.The resulting model from this framework is directly applicable to reinforcement learning,requiring only minor adjustments and optimizations to train a readily usable reinforcement learning model grounded in expert experience.Experimental validation in a two-dimensional dynamic-static hybrid target pursuit scenario demonstrates the method's impressive performance.The results in-dicate that the proposed method effectively assimilates expert knowledge,providing a high-starting-point and effective initial model for subsequent reinforcement learning training phases.
维普
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