AI BriefingLG AI Research 125
Key point
Introduces the latest reinforcement learning research trends for solving real-world problems, as discussed at ICLR 2021.
Details
At ICLR 2021, various studies dealing with the representation, learning, and optimization of deep learning models were presented. In particular, recent reinforcement learning research is shifting its focus beyond simple computer games toward real-world problems applicable to industrial settings, such as Robotics and goal-reaching tasks.
This article classifies reinforcement learning research for solving real-world problems into two key topics.
- Unsupervised Reinforcement Learning: Research aimed at addressing the difficulties in learning that arise in environments with sparse rewards.
- Reinforcement Learning with Self-Supervision: Research that increases learning efficiency and robustness through visual inputs such as images.
In particular, in real-world environments, rewards are often difficult to define or highly abstract. For example, when a robot arm learns to grasp an object, a binary success/failure reward alone makes it difficult to learn the complex process. To address this, research is progressing in a direction that minimizes human intervention.
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