Research Area: Machine Learning
Many real-world applications require an agent to make robust and deliberate decisions 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 methods difficult to learn a useful state representation in the end-to-end training with multimodal information. To address this, we propose a novel multimodal RL approach that can do multimodal alignment and importance enhancement according to their similarity and importance in terms of RL tasks respectively. By doing so, we are able to learn an effective state representation and consequentially improve the RL training process. We test our approach on several multimodal RL domains, showing that it outperforms state-of-the-art methods in terms of learning speed and policy quality.
Keywords:
Multimodal
Reinforcement Learning
Policy Quality
Modality
Enhancement
Author(s) Name: Jinming Ma, Feng Wu, Yingfeng Chen, Xianpeng Ji, Yu Ding
Journal name: Machine Learning
Conferrence name:
Publisher name: arXiv:2302.09318
DOI: https://doi.org/10.48550/arXiv.2302.09318
Volume Information: v1
Paper Link: https://arxiv.org/abs/2302.09318
