Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
This work addresses the need for more effective hierarchical imitation learning in robotics, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of learning hierarchical policies from unannotated demonstrations in long-horizon robotic tasks by developing a novel algorithm based on Adversarial Inverse Reinforcement Learning and Expectation-Maximization, achieving superior performance on challenging control tasks.
Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy with the option framework. Existing methods either overlook the causal relationship between the subtask and its corresponding policy or cannot learn the policy in an end-to-end fashion, which leads to suboptimality. In this work, we develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning and adapt it with the Expectation-Maximization algorithm in order to directly recover a hierarchical policy from the unannotated demonstrations. Further, we introduce a directed information term to the objective function to enhance the causality and propose a Variational Autoencoder framework for learning with our objectives in an end-to-end fashion. Theoretical justifications and evaluations on challenging robotic control tasks are provided to show the superiority of our algorithm. The codes are available at https://github.com/LucasCJYSDL/HierAIRL.