  We study the problem of offline Imitation Learning (IL) where an agent aims
to learn an optimal expert behavior policy without additional online
environment interactions. Instead, the agent is provided with a supplementary
offline dataset from suboptimal behaviors. Prior works that address this
problem either require that expert data occupies the majority proportion of the
offline dataset, or need to learn a reward function and perform offline
reinforcement learning (RL) afterwards. In this paper, we aim to address the
problem without additional steps of reward learning and offline RL training for
the case when demonstrations contain a large proportion of suboptimal data.
Built upon behavioral cloning (BC), we introduce an additional discriminator to
distinguish expert and non-expert data. We propose a cooperation framework to
boost the learning of both tasks, Based on this framework, we design a new IL
algorithm, where the outputs of discriminator serve as the weights of the BC
loss. Experimental results show that our proposed algorithm achieves higher
returns and faster training speed compared to baseline algorithms.
