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 ...

This chapter considers recent proposals that a related family of algorithms, called model-based reinforcement learning, may provide a similarly quantitative account for action choice by cognitive ...

However, many proposed reinforcement learning (RL) based algorithms take into account ... leading to isolation of agents and affecting algorithm convergence. Then, we propose a model-based RL ...

The reinforcement learning can qualify as the model-based if the machine learning algorithms can explicitly refer to the AI model. The archetypical model-based algorithms are known as dynamic ...

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.

A simple definition of reinforcement learning is to train the machine to act ... Starting with Q-Learning, which is a model-free and off-policy RL algorithm that is based on the Bellman Equation. The ...

Leveraging the OpenAI Gym environment, I used the Proximal Policy Optimization (PPO) algorithm to train the agent. A frame from Super Mario Bros. Environment The primary objective of the project was ...