An Introduction to Multi-Agent Reinforcement Learning

Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes. You will also learn what an agent is and how multi-agent systems can be both cooperative...

Reinforcement Learning for Engineers, Part 1: What Is Reinforcement Learning...

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...

Reinforcement Learning for Engineers, Part 2: Understanding the Environment ...

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...

Reinforcement Learning for Engineers, Part 3: Policies and Learning Algorith...

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...

Reinforcement Learning for Engineers, Part 4: The Walking Robot Problem

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...

MATLAB Example: Train Multiple Agents for Area Coverage

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...

Multi-agent reinforcement learning: An overview

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 complexity...

Multi-Agent Reinforcement Learning: Independent vs Cooperative Agents

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...

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

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...