  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.
