  Policy distillation, which transfers a teacher policy to a student policy has
achieved great success in challenging tasks of deep reinforcement learning.
This teacher-student framework requires a well-trained teacher model which is
computationally expensive. Moreover, the performance of the student model could
be limited by the teacher model if the teacher model is not optimal. In the
light of collaborative learning, we study the feasibility of involving joint
intellectual efforts from diverse perspectives of student models. In this work,
we introduce dual policy distillation(DPD), a student-student framework in
which two learners operate on the same environment to explore different
perspectives of the environment and extract knowledge from each other to
enhance their learning. The key challenge in developing this dual learning
framework is to identify the beneficial knowledge from the peer learner for
contemporary learning-based reinforcement learning algorithms, since it is
unclear whether the knowledge distilled from an imperfect and noisy peer
learner would be helpful. To address the challenge, we theoretically justify
that distilling knowledge from a peer learner will lead to policy improvement
and propose a disadvantageous distillation strategy based on the theoretical
results. The conducted experiments on several continuous control tasks show
that the proposed framework achieves superior performance with a learning-based
agent and function approximation without the use of expensive teacher models.

Machine Learning;Artificial Intelligence