Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning
This work addresses the problem of multitask imitation learning for robotics, offering an incremental extension to GAIL.
The authors tackled the limitation of Generative Adversarial Imitation Learning (GAIL) to single tasks by proposing situated GAIL (S-GAIL), which introduces a task variable to handle multiple tasks, and they demonstrated its effectiveness in acquiring multiple reward functions and policies in navigation and arm reaching experiments.
Generative adversarial imitation learning (GAIL) has attracted increasing attention in the field of robot learning. It enables robots to learn a policy to achieve a task demonstrated by an expert while simultaneously estimating the reward function behind the expert's behaviors. However, this framework is limited to learning a single task with a single reward function. This study proposes an extended framework called situated GAIL (S-GAIL), in which a task variable is introduced to both the discriminator and generator of the GAIL framework. The task variable has the roles of discriminating different contexts and making the framework learn different reward functions and policies for multiple tasks. To achieve the early convergence of learning and robustness during reward estimation, we introduce a term to adjust the entropy regularization coefficient in the generator's objective function. Our experiments using two setups (navigation in a discrete grid world and arm reaching in a continuous space) demonstrate that the proposed framework can acquire multiple reward functions and policies more effectively than existing frameworks. The task variable enables our framework to differentiate contexts while sharing common knowledge among multiple tasks.