diff --git a/units/en/unit7/introduction-to-marl.mdx b/units/en/unit7/introduction-to-marl.mdx index f0a3734..423c3a0 100644 --- a/units/en/unit7/introduction-to-marl.mdx +++ b/units/en/unit7/introduction-to-marl.mdx @@ -2,10 +2,10 @@ ## From single agent to multiple agents -From the first unit, we learned to train agents in a single-agent system. Where our agent was alone in its environment: **it was not cooperating or collaborating with other agents**. +In the first unit, we learned to train agents in a single-agent system. Where our agent was alone in its environment: **it was not cooperating or collaborating with other agents**.
-”Patchwork”/ +Patchwork
A patchwork of all the environments you've trained your agents on since the beginning of the course
@@ -16,19 +16,19 @@ When we do Multi agents reinforcement learning (MARL), we are in a situation whe For instance, you can think of a warehouse where **multiple robots need to navigate to load and unload packages**.
-”Warehouse”/ +Warehouse
Image by upklyak on Freepik
Or a road with **several autonomous vehicles**.
-”Self +Self driving cars
Image by jcomp on Freepik
-In these examples, we have multiple agents interacting in the environment and with the other agents. This implies defining a multi-agent system. But first, let's understand the different types of multi-agent environments. +In these examples, we have **multiple agents interacting in the environment and with the other agents**. This implies defining a multi-agents system. But first, let's understand the different types of multi-agent environments. ## Different types of multi-agent environments @@ -42,16 +42,16 @@ For instance, in a warehouse, **robots must collaborate to load and unload the p For example, in a game of tennis, **each agent wants to beat the other agent**. -”Tennis”/ + - *Mixed of both adversarial and cooperative*: like in our SoccerTwos environment, two agents are part of a team (blue or purple): they need to cooperate with each other and beat the opponent team.
-”SoccerTwos”/ +SoccerTwos
-
This environment was made by the Unity MLAgents Team
+
This environment was made by the Unity MLAgents Team
-So now we can ask how we design these multi-agent systems. Said differently, how can we train agents in a multi-agent setting? +So now we can ask how we design these multi-agent systems. Said differently, **how can we train agents in a multi-agents setting** ? diff --git a/units/en/unit7/introduction.mdx b/units/en/unit7/introduction.mdx index 677409c..86c18bd 100644 --- a/units/en/unit7/introduction.mdx +++ b/units/en/unit7/introduction.mdx @@ -1,25 +1,36 @@ # Introduction [[introduction]] -”Thumbnail”/ +Thumbnail -Since the beginning of this course, we learned to train agents in a single-agent system. Where our agent was alone in its environment: it was not cooperating or collaborating with other agents. +Since the beginning of this course, we learned to train agents in a *single-agent system* where our agent was alone in its environment: it was **not cooperating or collaborating with other agents**. -Our different agents worked great, and the single-agent system is useful for many applications. +This worked great, and the single-agent system is useful for many applications. -But, as humans, we live in a multi-agent world. Our intelligence comes from interaction with other agents. And so, our goal is to create agents that can interact with other humans and other agents. - -Consequently, we must study how to train deep reinforcement learning agents in a multi-agent system to build robust agents that can adapt, collaborate, or compete. - -So today, we’re going to learn the basics of this fascinating topic of multi-agents reinforcement learning (MARL). - -And the most exciting part is that during this unit, you’re going to train your first agents in a multi-agents system: a 2vs2 soccer team that needs to beat the opponent team. - -And you’re going to participate in AI vs. AI challenges where your trained agent will compete against other classmates’ agents every day and be ranked on a new leaderboard.
-”SoccerTwos”/ -
This environment was made by the Unity MLAgents Team
+Patchwork + +
+ +A patchwork of all the environments you’ve trained your agents on since the beginning of the course + +
+ +But, as humans, **we live in a multi-agent world**. Our intelligence comes from interaction with other agents. And so, our **goal is to create agents that can interact with other humans and other agents**. + +Consequently, we must study how to train deep reinforcement learning agents in a *multi-agents system* to build robust agents that can adapt, collaborate, or compete. + +So today, we’re going to **learn the basics of this fascinating topic of multi-agents reinforcement learning (MARL)**. + +And the most exciting part is that during this unit, you’re going to train your first agents in a multi-agents system: **a 2vs2 soccer team that needs to beat the opponent team**. + +And you’re going to participate in **AI vs. AI challenge** where your trained agent will compete against other classmates’ agents every day and be ranked on a [new leaderboard](). + +
+”SoccerTwos”/ + +
This environment was made by the Unity MLAgents Team