Learning latent structure: carving nature at its joints.

Area of Science:

• Neuroscience
• Artificial Intelligence
• Cognitive Science

Background:

• Reinforcement learning (RL) models decision-making and learning but is slow in complex, real-world scenarios.
• Animal and human learning significantly outperform current RL algorithms in complex environments.

Purpose of the Study:

• Investigate why RL algorithms are slow in complex tasks compared to biological learning.
• Explore how incorporating 'structure learning' can enhance RL performance.
• Focus on leveraging perceptual and action-based structure for improved learning.

Main Methods:

• Surveying the emerging literature on structure learning.
• Analyzing how inferred task structure can aid RL algorithms.
• Examining the role of structure in perception and action for learning.

Main Results:

• Identified task structure as a key factor in efficient learning for humans and animals.
• Proposed structure learning as a method to bridge the performance gap between RL and biological learning.
• Highlighted the importance of perceptual and action structure in simplifying complex learning problems.

Conclusions:

• Structure learning offers a promising avenue to enhance RL efficiency in complex, real-world tasks.
• Integrating insights from biological learning, particularly structure inference, can significantly advance RL.
• Future RL development should focus on algorithms that can infer and utilize task structure.