Model-free vs model-based reinforcement learning
Reinforcement learning is one of the exciting branches of artificial intelligence. It plays an important role in game-playing AI systems, modern robots, chip-design systems, and other applications.
There are many different types of reinforcement learning algorithms, but two main categories are “model-based” and “model-free” RL. They are both inspired by our understanding of learning in humans and animals.
Nearly every book on reinforcement learning contains a chapter that explains the differences between model-free and model-based reinforcement learning. But seldom are the biological and evolutionary precedents discussed in books about reinforcement learning algorithms for computers.
I found a very interesting explanation of model-free and model-based RL in The Birth of Intelligence, a book that explores the evolution of intelligence. In a conversation with TechTalks, Daeyeol Lee, neuroscientist and author of The Birth of Intelligence, discussed different modes of reinforcement learning in humans and animals, AI and natural intelligence, and future directions of research.
Read the full interview on TechTalks.
For more on reinforcement learning: