Especially model-free reinforcement learning algorithms are given in details and the differences of these algorithms are handled. Finally, some open problems in reinforcement learning are presented ...

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 ...

Many modern reinforcement learning algorithms are model-free, so they are applicable in different environments and can readily react to new and unseen states. In their seminal work on reinforcement ...

The two categories are called model-based reinforcement learning and model-free reinforcement learning. AI model learning is based on neural networks and machine learning algorithms to achieve a ...

- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto: a classic textbook that covers the fundamentals and algorithms of RL, including model-free and model-based methods.

In this paper we present techniques for centralized training of Multi-Agent (Deep) Reinforcement Learning (MARL) using the model-free Deep Q-Network as the baseline model and message sharing between ...

The researchers adapted a model-free reinforcement learning method called "deep deterministic policy gradients" (DDPG) and applied it to models of low-level and high-level neural dynamics. They ...