These are the resources that are referenced throughout the MATLAB Tech Talk video I made called "A gentle introduction to Multi-Agent Reinforcement Learning"
The video has not been published yet but I will add it to this journey once it is available.
Artificial intelligence, machine learning, deep neural networks. These are terms that can spark your imagination of a future where robots are thinking and evolving creatures.In this video...See More
In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a...See More
This video provides an introduction to the algorithms that reside within the agent. We’ll cover why we use neural networks to represent functions and why you may have to set up two neural...See More
This video shows how to use the reinforcement learning workflow to get a bipedal robot to walk. It also looks at how to modify the default example to make it look more like how one would set...See More
This example demonstrates a multi-agent collaborative-competitive task in which you train three proximal policy optimization (PPO) agents to explore all areas within a grid-world environment...See More
From the abstract:
Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The...See More
From the Abstract:
Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trialand -error, but also through cooperation by...See More
From the abstract
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular...See More