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Many real-world applications require an agent to make robust and deliberatedecisions with multimodal information (e.g., robots with multi-sensory inputs).However, it is very challenging to train the agent via reinforcement learning(RL) due to the heterogeneity and dynamic importance of different modalities.Specifically, we observe that these issues make conventional RL methodsdifficult to learn a useful state representation in the end-to-end trainingwith multimodal information. To address this, we propose a novel multimodal RLapproach that can do multimodal alignment and importance enhancement accordingto their similarity and importance in terms of RL tasks respectively. By doingso, we are able to learn an effective state representation and consequentiallyimprove the RL training process. We test our approach on several multimodal RLdomains, showing that it outperforms state-of-the-art methods in terms oflearning speed and policy quality.

Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement