It makes online probabilistic dynamic model inference based on Gaussian process regression and executes offline policy improvement using PPO on the inferred model. Empirical evaluation on the pendulum ...

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.

To make decisions, the rat just has to select at each state the action with the largest action value for that state. According to Barto & Sutton, the distinction between model-free and model-based ...

In this project, you will be asked to implement two model-free algorithms. The first one is Monte-Carlo(MC), including the first visit of on-policy MC prediction and on-policy MC control for blackjack ...

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 ...

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 ...

It makes online probabilistic dynamic model inference based on Gaussian process regression and executes offline policy improvement using PPO on the inferred model. Empirical evaluation on the pendulum ...