Reinforcement Learning, Fast and Slow.

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Neuroscience

Background:

• Deep reinforcement learning (RL) has achieved human-level performance in complex tasks.
• A key critique of deep RL is its sample inefficiency, questioning its plausibility as a model for human learning.
• Cognitive scientists are interested in understanding human learning through AI models.

Purpose of the Study:

• To address the critique of deep RL's sample inefficiency.
• To present recent techniques enabling faster deep RL.
• To explore the implications of these faster methods for psychology and neuroscience.

Main Methods:

• Review of recently developed techniques in deep reinforcement learning.
• Focus on methods that enhance the speed and efficiency of RL algorithms.
• Analysis of the connection between fast and incremental learning paradigms.

Main Results:

• New techniques allow deep RL to learn much more quickly.
• These advancements counter the argument that deep RL is too slow to model human learning.
• Demonstration of a fundamental link between rapid and gradual learning processes.

Conclusions:

• Faster deep RL methods are emerging, making them more plausible models for human learning.
• These AI-driven techniques offer valuable insights for psychology and neuroscience.
• The study highlights the interconnectedness of fast and slow learning mechanisms.