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Recently, the bidirectional encoder representations from transformers (BERT)model has attracted much attention in the field of natural language processing,owing to its high performance in language understanding-related tasks. The BERTmodel learns language representation that can be adapted to various tasks viapre-training using a large corpus in an unsupervised manner. This studyproposes the language and action learning using multimodal BERT (lamBERT) modelthat enables the learning of language and actions by 1) extending the BERTmodel to multimodal representation and 2) integrating it with reinforcementlearning. To verify the proposed model, an experiment is conducted in a gridenvironment that requires language understanding for the agent to act properly.As a result, the lamBERT model obtained higher rewards in multitask settingsand transfer settings when compared to other models, such as the convolutionalneural network-based model and the lamBERT model without pre-training.

lamBERT: Language and Action Learning Using Multimodal BERT