AI BriefingLG AI Research 207
Key point
This introduces research that combines Reinforcement Learning (RL) with Graph Neural Networks (GNN) to process complex data and learn general-purpose policies.
Details
LG AI Research's Sunghoon Hong is conducting research that combines Reinforcement Learning (RL) with Graph Neural Networks (GNN) to effectively represent complex real-world data.
Existing reinforcement learning methods have a limitation of low data efficiency because they learn a single policy to solve only a specific problem. To overcome this, the key is to build a Single General-purpose Policy that can operate across various robots or environments.
In particular, when controlling objects with complex structures such as robots composed of joints, this research captures data as a graph structure to reflect the connectivity and correlations between nodes. Based on this, it presents a method to improve the efficiency of multi-task reinforcement learning, building on the research 'Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning' presented at ICLR 2022.
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