  Task-oriented dialogue (TOD) systems function as digital assistants, guiding
users through various tasks such as booking flights or finding restaurants.
Existing toolkits for building TOD systems often fall short of in delivering
comprehensive arrays of data, models, and experimental environments with a
user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue
system toolkit crafted to bridge this gap. Our unified data format simplifies
the integration of diverse datasets and models, significantly reducing
complexity and cost for studying generalization and transfer. Enhanced with
robust reinforcement learning (RL) tools, featuring a streamlined training
process, in-depth evaluation tools, and a selection of user simulators,
ConvLab-3 supports the rapid development and evaluation of robust dialogue
policies. Through an extensive study, we demonstrate the efficacy of transfer
learning and RL and showcase that ConvLab-3 is not only a powerful tool for
seasoned researchers but also an accessible platform for newcomers.
