  Partial domain adaptation aims to transfer knowledge from a label-rich source
domain to a label-scarce target domain which relaxes the fully shared label
space assumption across different domains. In this more general and practical
scenario, a major challenge is how to select source instances in the shared
classes across different domains for positive transfer. To address this issue,
we propose a Domain Adversarial Reinforcement Learning (DARL) framework to
automatically select source instances in the shared classes for circumventing
negative transfer as well as to simultaneously learn transferable features
between domains by reducing the domain shift. Specifically, in this framework,
we employ deep Q-learning to learn policies for an agent to make selection
decisions by approximating the action-value function. Moreover, domain
adversarial learning is introduced to learn domain-invariant features for the
selected source instances by the agent and the target instances, and also to
determine rewards for the agent based on how relevant the selected source
instances are to the target domain. Experiments on several benchmark datasets
demonstrate that the superior performance of our DARL method over existing
state of the arts for partial domain adaptation.
