Abstract: Policy distillation,a method of transferring knowledge from one policy to another,has achieved great success in challenging reinforcement learning tasks.The typical policy distillation approach uses a teacher-student policy model,where know-ledge is transferred from the teacher policy,which has excellent empirical data,to the student policy.Obtaining a teacher policy is computationally intensive,so dual policy distillation(DPD) framework is proposed,which maintains two student policies to transfer knowledge to each other and no longer depends on the teacher policy.However,if one of the student policies cannot surpass the other through self-learning,or if the two student policies converge after distillation,the deep reinforcement learning algorithm combined with DPD degenerates into a single policy gradient optimization approach.To address the problems mentioned above,the concept of similarity between student policies is given,and the similarity constrained dual policy distillation(SCDPD) framework is proposed.The framework dynamically adjusts the similarity between two students' policies in the process of knowledge transfer,and has been theoretically shown to be effective in enhancing the exploration of students′ policies as well as the stability of algorithms.Experimental results show that the SCDPD-SAC algorithm and SCDPD-PPO algorithm,which combine SCDPD with classical off-policy and on-policy deep reinforcement learning algorithms,have better performance compared with classical algorithms on multiple continuous control tasks.

Key words: Deepre inforcement learning, Policy distillation, Similarity constraint, Knowledge transfer, Continuous control tasks