AI BriefingLG AI Research 631
Key point
LG AI Research unveiled a multi-phase reinforcement learning framework and RL-Studio optimized for complex industrial settings.
Details
Among the key recent trends in AI research, Physical AI and Embodied AI are rapidly emerging, increasing the importance of Reinforcement Learning (RL), which is central to robot control.
Existing RL has evolved from Online RL, which interacts directly with the environment, to Offline RL, which uses already-collected data, and further to Offline-to-Online RL, which combines the two. However, the existing fixed two-stage (Offline → Online) process has limits in handling the complex variables of real industrial settings.
To overcome these limitations, LG AI Research's Physical Intelligence (PI) Lab has proposed a Multi-Phase RL framework. This approach flexibly switches learning styles (Offline ↔ Online) or algorithms (TD3 → SAC, etc.) to find the optimal policy.
Along with this, RL-Studio was also released—a reinforcement learning experimentation platform that supports researchers in experimenting with and managing various learning stages and algorithm transitions.
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