4-2. Introduction to Multi-Agent RL

The field of multi-agent RL is abuzz with cutting edge research. Recently, Open AI announced that its team of five neural networks, OpenAI 5 has learned to defeat amature DoTA 2 players. OpenAI 5 has been trained using a scaled-up version of BPO. Coordination between agents is controlled using a hyperparameter called team spirit. It … Read more

For this video, let’s pretend that you and your sister are playing a game of ball. You are given one bank or 100 coins from which you plan on buying a video game console. For each time either of you misses the ball, you lose one coin from the bank to your parents. Hence, you … Read more

So, can we think about adapting the single-agent auto techniques we’ve learned about so far to the multi-agent case? Two extreme approaches come to mind. The simplest approach should be to train all the agents independently without considering the existence of other agents. In this approach, any agent considers all the others to be a … Read more

Consider an example of single agent reinforcement learning. We have a drone with the task of grabbing a package. The possible actions are going right, left, up, down, and grasping. The reward is plus 50 for grasping the package, at minus one otherwise. Now, the difference in multi-agent RL, is that we have more than … Read more

Hi, all. Having multiple agents in a system brings in a few benefits. The agents can share their experiences with one another making each other smarter, just as we learned from our teachers and friends. However, when agents want to share, they have to communicate, which leads to a cost of communication, like extra hardware … Read more

In this video, we will discuss some potential real-life applications of multi-agent systems. A group of drones or robots whose aim is to pick up a package and drop it to the destination is a multi-agent system. In the stock market, each person who is trading can be considered as an agent and the profit … Read more

In this video, we will seek some motivation for why we should consider multiple agents in the context of Artificial Intelligence. Keep in mind that the ultimate goal of AI is to solve intelligence. We live in a multi agent world, we do not become intelligent in isolation. As a baby, the closest interactions that … Read more

In this video, we are going to get an understanding of multi-agent systems. Multi-agent systems are present everywhere around us, be it early in the morning when you’re making your way through traffic to get to work or when your favorite soccer players are competing in a game or when a swarm of bees is … Read more

Hey, everyone. With this, we’ve reached the end of the exciting module on Multi-agent RL. We began by introducing ourselves to the multi-agent systems present in our surroundings. We reasoned why multi-agent systems are an important puzzle to solving AI, and decided to pursue this complex topic. We also studied the Markov games framework, which … Read more

The normal agents are rewarded based on the least distance of any of the agents to the landmark, and penalized based on the distance between the adversary and the target landmark. Under this reward structure, the agents cooperate to spread out across all the landmarks, so as to deceive the adversary. The framework of centralized … Read more

Hi everyone. The paper that you’ve chosen implements a multi-agent version of DDPG. DDPG, as you might remember, is an off policy actor-critic algorithm that uses the concept of target networks. The input of the action network is the current state while the output is a real value or a vector representing an action chosen … Read more

Hi everyone, I’m Chhavi, a Content Developer at Udacity. I’m also a computer science graduate student. In this section, I’ll be breaking down multiagent reinforcement learning. Later on, we’ll also do a coding exercise to make it clear how such systems work. Let’s get started.