Research On Generative Adversarial Imitation Learning For Multi-Type Expert Demonstrations

Imitation learning aims to learn an optimal policy from expert demonstrations to guide an agent to complete a specific task.In recent years,imitation learning methods have developed from Behavior Cloning to Inverse Reinforcement Learning and Generative Adversarial Imitation Learning(GAIL),and have been widely applied in fields such as natural language processing,autonomous driving systems,and robot control technology.Existing imitation learning methods typically require a large number of high-quality expert demonstrations as the imitation target,and are limited to imitating expert behaviors from a single,independent demonstration.However,in practical applications,available demonstrations may have various unexpected situations,such as limited quantity and uneven quality of demonstrations,multi-task demonstrations and unstructured demonstrations.To solve the problem,this paper takes the perspective of expert demonstrations and proposes related algorithms based on GAIL.The main content can be summarized into three parts.(1)An algorithm,referred to as Non-negative Positive-unlabeled Generative Adversarial Imitation Learning(PUIWIL),is proposed to address the problem of uneven quality of expert demonstrations.PUIWIL introduces confidence scores to measure the quality of demonstrations and uses Non-negative Positive-unlabeled Learning to train a confidence evaluator to assign confidence scores to demonstrations.Then,the importance sampling idea is used to assign different priorities to demonstrations for GAIL.PUIWIL can not only control the agent to focus on imitating high-quality demonstrations,but also make full use of effective information in relatively low-quality demonstrations.The experiments demonstrate that PUIWIL can effectively improve the performance of imitation learning from multiple quality demonstrations.(2)In practical applications,due to the complexity of the environment,there are usually demonstrations provided by experts with different skills and habits from different tasks.For this,the Meta Generative Adversarial Imitation Learning from Demonstrations for Multitask(MILD)algorithm is proposed.By introducing meta-learning,the learning process is divided into two stages:meta-training and meta-testing.In the meta-training stage,the agent continuously synthesizes the optimization directions of the model on various tasks by sharing information from multi-task demonstrations,for adaptation across tasks.In the meta-testing stage,in order to enable efficient learning of new tasks,MILD fine-tunes the model trained in the meta-training stage and utilizes the experienced knowledge learned from the source tasks to improve its learning speed and performance on the new task.The experiments demonstrate that the MILD can effectively handle multi-task demonstrations,not only reducing the number of demonstrations required for imitation learning on each task,but also accomplishing joint imitation learning while improving the generalization of the imitation learning model.(3)Although MILD can handle multi-task demonstrations,it requires that the demonstrations from different tasks are independent of each other.However,in real-world scenarios,unprocessed multi-task demonstrations may be unstructured,meaning that there may be multiple task trajectories in a single demonstration.To address this problem,the Information Maximizing Meta Imitation Learning from Unstructured Demonstrations(Info-MIL)algorithm is proposed.First,Info-MIL introduces latent codes to maximize the mutual information between codes and trajectories,allowing the codes to represent the latent features of demonstrations and to distinguish between different tasks in an unsupervised manner.Then,Info-MIL uses meta-GAIL to imitate the demonstrations from each task.The experiments demonstrate that Info-MIL can effectively distinguish between different tasks from unstructured demonstrations and complete joint imitation learning.This paper conducted extensive experiments and analyses,verifying the effectiveness and superiority of the methods proposed in this paper.