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Multi-Agent Reinforcement Learning
Mat Leonard,
Miguel Morales,
Chhavi Yadav,
Dana Sheahan,
Cezanne Camacho,
Alexis Cook,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Mat Leonard,
Miguel Morales,
Chhavi Yadav,
Dana Sheahan,
Cezanne Camacho,
Alexis Cook,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/jmaqt7/multi
What's inside
Syllabus
Obtain helpful resources to accelerate your learning in the fourth part of the Nanodegree program.
Introduction to Multi-Agent RL
Case Study: AlphaZero
Read more
Syllabus
Obtain helpful resources to accelerate your learning in the fourth part of the Nanodegree program.
Introduction to Multi-Agent RL
Case Study: AlphaZero
Read more
Train a pair of agents to play tennis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides the resources to enhance learning progress
Explores the fundamentals of Multi-Agent RL, providing learners with a foundation in this specialized area
Incorporates a case study on AlphaZero, a significant breakthrough in artificial intelligence, exposing learners to cutting-edge applications
Provides hands-on experience through a practical exercise where learners train agents to play tennis, reinforcing theoretical concepts
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Activities
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We're preparing activities for
Multi-Agent Reinforcement Learning.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Multi-Agent Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Private Equity Investor
Private Equity Investor
Private Equity Investors invest in private companies. They work with companies to provide capital and expertise to help them grow and succeed. The Multi-Agent Reinforcement Learning course may be useful for Private Equity Investors who want to learn about new techniques for investing in private companies. This course can help Private Equity Investors build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of private equity investment problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. These models can be used to solve a wide range of problems, including image recognition, natural language processing, and fraud detection. The Multi-Agent Reinforcement Learning course may be useful for Machine Learning Engineers who want to learn about new techniques for developing machine learning models. This course can help Machine Learning Engineers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of machine learning problems.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks. They work with organizations to develop strategies to reduce the impact of risks on the organization. The Multi-Agent Reinforcement Learning course may be useful for Risk Managers who want to learn about new techniques for identifying and assessing risks. This course can help Risk Managers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of risk management problems.
Venture Capitalist
Venture Capitalist
Venture Capitalists invest in early-stage companies. They work with entrepreneurs to provide capital and expertise to help them develop and grow their businesses. The Multi-Agent Reinforcement Learning course may be useful for Venture Capitalists who want to learn about new techniques for investing in early-stage companies. This course can help Venture Capitalists build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of venture capital investment problems.
Investment Analyst
Investment Analyst
Investment Analysts analyze investment opportunities and make recommendations to clients. They work with individuals, families, and institutions to manage their investment portfolios. The Multi-Agent Reinforcement Learning course may be useful for Investment Analysts who want to learn about new techniques for analyzing investment opportunities. This course can help Investment Analysts build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of investment analysis problems.
Hedge Fund Manager
Hedge Fund Manager
Hedge Fund Managers manage hedge funds, which are investment funds that use advanced investment strategies to generate high returns. Hedge Fund Managers work with investors to raise capital and invest in a variety of assets. The Multi-Agent Reinforcement Learning course may be useful for Hedge Fund Managers who want to learn about new techniques for managing hedge funds. This course can help Hedge Fund Managers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of hedge fund management problems.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment decisions. They work with companies, investors, and other stakeholders to provide insights into the financial performance of companies and industries. The Multi-Agent Reinforcement Learning course may be useful for Financial Analysts who want to learn about new techniques for analyzing financial data. This course can help Financial Analysts build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of financial analysis problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They develop models to predict future financial performance and make investment decisions. The Multi-Agent Reinforcement Learning course may be useful for Quantitative Analysts who want to learn about new techniques for analyzing financial data. This course can help Quantitative Analysts build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of financial analysis problems.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop products and services that meet the needs of clients. The Multi-Agent Reinforcement Learning course may be useful for Actuaries who want to learn about new techniques for assessing risk and uncertainty. This course can help Actuaries build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of actuarial problems.
Investment Banker
Investment Banker
Investment Bankers work with companies to raise capital and advise on mergers and acquisitions. They work with a variety of clients, including corporations, governments, and institutions. The Multi-Agent Reinforcement Learning course may be useful for Investment Bankers who want to learn about new techniques for advising clients on capital raising and mergers and acquisitions. This course can help Investment Bankers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of investment banking problems.
Portfolio Manager
Portfolio Manager
Portfolio Managers manage investment portfolios for clients. They work with clients to develop investment strategies and make investment decisions. The Multi-Agent Reinforcement Learning course may be useful for Portfolio Managers who want to learn about new techniques for managing investment portfolios. This course can help Portfolio Managers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of portfolio management problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. The Multi-Agent Reinforcement Learning course may be useful for Software Engineers who want to learn about new techniques for developing software. This course can help Software Engineers build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of software development problems.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, analyze, and interpret data to identify trends and patterns. The Multi-Agent Reinforcement Learning course may be useful for Data Scientists who want to learn about new techniques for analyzing data. This course can help Data Scientists build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of data science problems.
Consultant
Consultant
Consultants provide advice and expertise to organizations on a variety of topics. They work with clients to identify problems, develop solutions, and implement change. The Multi-Agent Reinforcement Learning course may be useful for Consultants who want to learn about new techniques for providing advice and expertise to clients. This course can help Consultants build a foundation in the fundamentals of reinforcement learning, which can be applied to a variety of consulting problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use advanced analytical techniques to help organizations make better decisions. These techniques can be applied to a wide range of problems, including resource allocation, scheduling, and supply chain management. The Multi-Agent Reinforcement Learning course may be useful for Operations Research Analysts who want to learn about new techniques for solving complex problems.
For more career information including salaries, visit:
OpenCourser.com/course/jmaqt7/multi
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Multi-Agent Reinforcement Learning.
This classic textbook provides a clear and comprehensive introduction to the field of reinforcement learning. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of algorithms for reinforcement learning. It valuable resource for researchers and advanced students in the field.
Provides a probabilistic perspective on machine learning. It valuable resource for students and practitioners who want to understand the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of multi-agent systems. It valuable resource for students and practitioners in the field.
Save
Provides a clear and comprehensive introduction to game theory. It valuable resource for students and practitioners in the field.
Save
Provides a comprehensive overview of the axiomatic theory of cooperative games. It valuable resource for researchers and advanced students in the field.
Provides a clear and comprehensive introduction to distributed artificial intelligence. It valuable resource for students and practitioners in the field.
Provides a practical introduction to machine learning. It valuable resource for students and practitioners who want to apply machine learning to real-world problems.
Save
Provides a comprehensive overview of deep learning. It valuable resource for students and practitioners who want to understand the theoretical foundations of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jmaqt7/multi
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Time
30240 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Miguel Morales
Chhavi Yadav
Dana Sheahan
Cezanne Camacho
Alexis Cook
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Provides the resources to enhance learning progress
Explores the fundamentals of Multi-Agent RL, providing learners with a foundation in this specialized area
Incorporates a case study on AlphaZero, a significant breakthrough in artificial intelligence, exposing learners to cutting-edge applications
Provides hands-on experience through a practical exercise where learners train agents to play tennis, reinforcing theoretical concepts
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