Study Plan

The fourth part of this nanodegree program covers multi-agent deep reinforcement learning and lasts 3 weeks. You can find all of the coding exercises from the lessons in this GitHub repository.

## Week 1

Most of reinforcement learning is concerned with a single agent that must demonstrate proficiency at a task. In that scenario, there are no other agents. However, if we’d like our agents to become truly intelligent, they must be able to communicate with — and learn from — other agents. Multi-agent reinforcement learning has many real-world applications, ranging from self-driving cars to warehouse management.

Lesson: Introduction to Multi-Agent RL

In this lesson, you’ll also explore frameworks and techniques that can be used to train multiple, interacting agents, through a research area known as multi-agent reinforcement learning.

## Weeks 2-3

During the final two weeks, you will focus on the project. You will also learn about AlphaZero.

Lesson: Case Study: AlphaZero (Optional)

In this lesson, you will learn all about Monte Carlo Tree Search (MCTS) and master the skills behind DeepMind's AlphaZero.