There are many algorithms for reinforcement learning, both model-based (e.g. dynamic programming) and model-free (e.g. Monte Carlo). Model-free methods tend to be more useful for actual ...

And we have much more than just model-free and model-based reinforcement learning, Lee believes. “I think our brain is a pandemonium of learning algorithms that have evolved to handle many ...

Q-learning is a model-free, value-based, off-policy algorithm for reinforcement learning that will find the best series of actions based on the current state. The “Q” stands for quality.

To remedy it somewhat in the video gaming domain, researchers at Google recently proposed a new algorithm — Simulated Policy Learning, or SimPLe for short — which uses game models to learn ...

Model-based algorithms: Model-based algorithms take a different approach to reinforcement learning. Instead of evaluating the value of states and actions, they try to predict the state of the ...

A new study on meta reinforcement learning algorithms helps us understand how the human brain learns to adapt to complexity and uncertainty when learning and making decisions. A research team ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.