Cross-domain Random Pre-training with Prototypes for Reinforcement Learning

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Task-agnostic cross-domain pre-training shows great potential in image-based Reinforcement Learning (RL) but poses a big challenge. In this paper, we propose CRPTpro, a Cross-domain self-supervised Random Pre-Training framework with prototypes for image-based RL. CRPTpro employs cross-domain random policy to easily and quickly sample diverse data from multiple domains, to improve pre-training efficiency. Moreover, prototypical representation learning with a novel intrinsic loss is proposed to pre-train an effective and generic encoder across different domains. Without finetuning, the cross-domain encoder can be implemented for challenging downstream visual-control RL tasks defined in different domains efficiently. Compared with prior arts like APT and Proto-RL, CRPTpro achieves better performance on cross-domain downstream RL tasks without extra training on exploration agents for expert data collection, greatly reducing the burden of pre-training. Experiments on DeepMind Control suite (DMControl) demonstrate that CRPTpro outperforms APT significantly on 11/12 cross-domain RL tasks with only 39% pre-training hours, becoming a state-of-the-art cross-domain pre-training method in both policy learning performance and pre-training efficiency. The complete code will be released at https://github.com/liuxin0824/CRPTpro.